default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID34th NeurIPS 2020
- Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020 - Seongmin Ok:
A graph similarity for deep learning. - Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy T. Chinen:
An Unsupervised Information-Theoretic Perceptual Quality Metric. - Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelovic, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman:
Self-Supervised MultiModal Versatile Networks. - Simiao Ren, Willie Padilla, Jordan M. Malof:
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method. - Masatoshi Uehara, Masahiro Kato, Shota Yasui:
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. - Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov:
Neural Methods for Point-wise Dependency Estimation. - Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. - Yiwen Guo, Qizhang Li, Hao Chen:
Backpropagating Linearly Improves Transferability of Adversarial Examples. - Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le:
PyGlove: Symbolic Programming for Automated Machine Learning. - Tamás Erdélyi, Cameron Musco, Christopher Musco:
Fourier Sparse Leverage Scores and Approximate Kernel Learning. - Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma:
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. - Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez:
Synbols: Probing Learning Algorithms with Synthetic Datasets. - Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Adversarially Robust Streaming Algorithms via Differential Privacy. - Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong:
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering. - Yuntian Deng, Alexander M. Rush
:
Cascaded Text Generation with Markov Transformers. - Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum:
Improving Local Identifiability in Probabilistic Box Embeddings. - Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. - William Gilpin:
Deep reconstruction of strange attractors from time series. - Shengxi Li, Zeyang Yu, Min Xiang, Danilo P. Mandic:
Reciprocal Adversarial Learning via Characteristic Functions. - Jiexin Duan, Xingye Qiao, Guang Cheng:
Statistical Guarantees of Distributed Nearest Neighbor Classification. - Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. - Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
The Statistical Complexity of Early-Stopped Mirror Descent. - Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. - Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. - Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. - Santiago Mazuelas, Andrea Zanoni, Aritz Pérez:
Minimax Classification with 0-1 Loss and Performance Guarantees. - Pierluca D'Oro, Wojciech Jaskowski:
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. - Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi:
Coresets for Regressions with Panel Data. - Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. - Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. - Yaniv Romano, Stephen Bates, Emmanuel J. Candès:
Achieving Equalized Odds by Resampling Sensitive Attributes. - Wenhao Luo, Wen Sun, Ashish Kapoor:
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates. - Pierre-Cyril Aubin-Frankowski, Zoltán Szabó:
Hard Shape-Constrained Kernel Machines. - Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-Lai Chung:
A Closer Look at the Training Strategy for Modern Meta-Learning. - Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel:
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. - Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen:
Generalised Bayesian Filtering via Sequential Monte Carlo. - Kai Han, Zongmai Cao, Shuang Cui, Benwei Wu:
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time. - Johann Brehmer, Kyle Cranmer:
Flows for simultaneous manifold learning and density estimation. - Austin Xu, Mark A. Davenport:
Simultaneous Preference and Metric Learning from Paired Comparisons. - Jincheng Bai, Qifan Song, Guang Cheng:
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. - Yufan Zhou, Changyou Chen, Jinhui Xu:
Learning Manifold Implicitly via Explicit Heat-Kernel Learning. - Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou:
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network. - Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian:
One-bit Supervision for Image Classification. - Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? - Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrák:
Submodular Maximization Through Barrier Functions. - Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. - Jinheon Baek, Dong Bok Lee, Sung Ju Hwang:
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction. - Kaustav Kundu, Joseph Tighe:
Exploiting weakly supervised visual patterns to learn from partial annotations. - Yibo Yang, Robert Bamler, Stephan Mandt:
Improving Inference for Neural Image Compression. - Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon:
Neuron Merging: Compensating for Pruned Neurons. - Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. - Arthur Delarue, Ross Anderson, Christian Tjandraatmadja:
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. - Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu:
Towards Playing Full MOBA Games with Deep Reinforcement Learning. - Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang:
Rankmax: An Adaptive Projection Alternative to the Softmax Function. - Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran:
Online Agnostic Boosting via Regret Minimization. - Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun:
Causal Intervention for Weakly-Supervised Semantic Segmentation. - Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon:
Belief Propagation Neural Networks. - Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora:
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. - Adil Khan, Khadija Fraz:
Post-training Iterative Hierarchical Data Augmentation for Deep Networks. - Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim:
Debugging Tests for Model Explanations. - Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis:
Robust compressed sensing using generative models. - Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. - Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine:
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. - Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. - Thiparat Chotibut, Fryderyk Falniowski, Michal Misiurewicz, Georgios Piliouras:
The route to chaos in routing games: When is price of anarchy too optimistic? - Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Online Algorithm for Unsupervised Sequential Selection with Contextual Information. - Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu:
Adapting Neural Architectures Between Domains. - Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise feature importance for time-series black-box models. - Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li:
Towards Better Generalization of Adaptive Gradient Methods. - Tanmay Gangwani, Yuan Zhou, Jian Peng:
Learning Guidance Rewards with Trajectory-space Smoothing. - Chaobing Song, Yong Jiang, Yi Ma:
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization. - Rishi Sonthalia, Anna C. Gilbert:
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding. - Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker:
Deep Structural Causal Models for Tractable Counterfactual Inference. - Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. - Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez:
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. - Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson:
Better Set Representations For Relational Reasoning. - Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing:
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning. - Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness:
A Combinatorial Perspective on Transfer Learning. - Amit Daniely, Gal Vardi:
Hardness of Learning Neural Networks with Natural Weights. - Steinar Laenen, He Sun:
Higher-Order Spectral Clustering of Directed Graphs. - Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone:
Primal-Dual Mesh Convolutional Neural Networks. - Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. - Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu:
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks. - Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani:
Sinkhorn Barycenter via Functional Gradient Descent. - Murad Tukan, Alaa Maalouf, Dan Feldman:
Coresets for Near-Convex Functions. - Bobby He, Balaji Lakshminarayanan, Yee Whye Teh:
Bayesian Deep Ensembles via the Neural Tangent Kernel. - Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. - Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. - Jiangxin Dong, Stefan Roth, Bernt Schiele:
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring. - Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver:
Discovering Reinforcement Learning Algorithms. - Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel:
Taming Discrete Integration via the Boon of Dimensionality. - Chenyang Lei, Yazhou Xing, Qifeng Chen:
Blind Video Temporal Consistency via Deep Video Prior. - Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin:
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. - Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin A. Carterette, Mounia Lalmas:
Model Selection for Production System via Automated Online Experiments. - Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher:
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems. - Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh:
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. - Luke I. Rast, Jan Drugowitsch:
Adaptation Properties Allow Identification of Optimized Neural Codes. - Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. - Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang:
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. - Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. - Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen:
Online Influence Maximization under Linear Threshold Model. - Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young:
Ensembling geophysical models with Bayesian Neural Networks. - Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin:
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation. - Christopher Frye, Colin Rowat, Ilya Feige:
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. - Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song:
Understanding Deep Architecture with Reasoning Layer. - Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko:
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. - Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein:
Detection as Regression: Certified Object Detection with Median Smoothing. - Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni:
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. - Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann:
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks. - Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun:
FleXOR: Trainable Fractional Quantization. - Eran Malach, Shai Shalev-Shwartz:
The Implications of Local Correlation on Learning Some Deep Functions. - Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson:
Learning to search efficiently for causally near-optimal treatments. - Ambar Pal, René Vidal:
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses. - Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. - Johannes Bausch:
Recurrent Quantum Neural Networks. - Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras:
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix. - Gergely Neu, Ciara Pike-Burke:
A Unifying View of Optimism in Episodic Reinforcement Learning. - Moran Feldman, Amin Karbasi:
Continuous Submodular Maximization: Beyond DR-Submodularity. - Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric:
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. - Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger:
Assessing SATNet's Ability to Solve the Symbol Grounding Problem. - Michal Jamroz, Marcin Kurdziel, Mateusz Opala:
A Bayesian Nonparametrics View into Deep Representations. - Amnon Geifman, Abhay Kumar Yadav, Yoni Kasten, Meirav Galun, David W. Jacobs, Ronen Basri:
On the Similarity between the Laplace and Neural Tangent Kernels. - Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik:
A causal view of compositional zero-shot recognition. - Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré:
HiPPO: Recurrent Memory with Optimal Polynomial Projections. - Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao:
Auto Learning Attention. - Trent Kyono, Yao Zhang, Mihaela van der Schaar:
CASTLE: Regularization via Auxiliary Causal Graph Discovery. - Kaihua Tang, Jianqiang Huang, Hanwang Zhang:
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. - Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou:
Explainable Voting. - Chun Kai Ling, Fei Fang, J. Zico Kolter:
Deep Archimedean Copulas. - Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy:
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. - Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi:
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging. - Shaogang Ren, Weijie Zhao, Ping Li:
Thunder: a Fast Coordinate Selection Solver for Sparse Learning. - Ziyin Liu, Tilman Hartwig, Masahito Ueda:
Neural Networks Fail to Learn Periodic Functions and How to Fix It. - Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen:
Distribution Matching for Crowd Counting. - Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov:
Correspondence learning via linearly-invariant embedding. - Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu:
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. - Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. - Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani:
Sinkhorn Natural Gradient for Generative Models. - Arthur Mensch, Gabriel Peyré:
Online Sinkhorn: Optimal Transport distances from sample streams. - Marc T. Law, Jos Stam:
Ultrahyperbolic Representation Learning. - Ilja Kuzborskij, Nicolò Cesa-Bianchi:
Locally-Adaptive Nonparametric Online Learning. - Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou:
Compositional Generalization via Neural-Symbolic Stack Machines. - Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro:
Graphon Neural Networks and the Transferability of Graph Neural Networks. - Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi:
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms. - Michael Janner, Igor Mordatch, Sergey Levine:
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction. - Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong:
Deep Transformers with Latent Depth. - Kunal Gupta, Manmohan Chandraker:
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows. - Jérôme-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. - Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam:
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. - Kai Jia, Martin C. Rinard:
Efficient Exact Verification of Binarized Neural Networks. - Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan:
Ultra-Low Precision 4-bit Training of Deep Neural Networks. - Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. - Xi Zhang, Xiaolin Wu:
On Numerosity of Deep Neural Networks. - Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. - Jiuxiang Gu, Jason Kuen, Shafiq R. Joty, Jianfei Cai, Vlad I. Morariu, Handong Zhao, Tong Sun:
Self-Supervised Relationship Probing. - Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng:
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback. - Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun:
Prophet Attention: Predicting Attention with Future Attention. - Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei:
Language Models are Few-Shot Learners. - Allan Grønlund, Lior Kamma, Kasper Green Larsen:
Margins are Insufficient for Explaining Gradient Boosting. - Alex Tseng, Avanti Shrikumar, Anshul Kundaje:
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics. - Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. - Zaheen Farraz Ahmad, Levi Lelis, Michael Bowling:
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces. - Peng Chen, Omar Ghattas:
Projected Stein Variational Gradient Descent. - Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi:
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks. - Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling:
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. - Masashi Tsubaki, Teruyasu Mizoguchi:
On the equivalence of molecular graph convolution and molecular wave function with poor basis set. - Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman:
The Power of Predictions in Online Control. - Tushar Nagarajan, Kristen Grauman:
Learning Affordance Landscapes for Interaction Exploration in 3D Environments. - Ilai Bistritz, Nicholas Bambos:
Cooperative Multi-player Bandit Optimization. - Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida:
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits. - Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov:
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout. - Steffen Czolbe, Oswin Krause, Ingemar J. Cox, Christian Igel:
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model. - Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake:
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. - Guannan Qu, Yiheng Lin, Adam Wierman, Na Li:
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward. - Akshunna S. Dogra, William T. Redman:
Optimizing Neural Networks via Koopman Operator Theory. - Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet:
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. - Jeremias Sulam, Ramchandran Muthukumar, Raman Arora:
Adversarial Robustness of Supervised Sparse Coding. - Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Meta-Learning of Bandit Policies. - Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio:
Biologically Inspired Mechanisms for Adversarial Robustness. - Surbhi Goel, Aravind Gollakota, Adam R. Klivans:
Statistical-Query Lower Bounds via Functional Gradients. - Yu Bai, Chi Jin, Tiancheng Yu:
Near-Optimal Reinforcement Learning with Self-Play. - Shushan He, Hongyuan Zha, Xiaojing Ye:
Network Diffusions via Neural Mean-Field Dynamics. - Zhilu Zhang, Mert R. Sabuncu:
Self-Distillation as Instance-Specific Label Smoothing. - Yunbei Xu, Assaf Zeevi:
Towards Problem-dependent Optimal Learning Rates. - Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu:
Cross-lingual Retrieval for Iterative Self-Supervised Training. - Diego P. P. Mesquita, Amauri H. Souza Jr., Samuel Kaski:
Rethinking pooling in graph neural networks. - Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. - Yao Zhang, Mihaela van der Schaar:
Gradient Regularized V-Learning for Dynamic Treatment Regimes. - Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré:
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence. - Veronica Chelu, Doina Precup, Hado van Hasselt:
Forethought and Hindsight in Credit Assignment. - Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. - Diego M. Arribas, Yuan Zhao, Il Memming Park:
Rescuing neural spike train models from bad MLE. - Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. - Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. - Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson L. S. Wong, Rose Yu:
Deep Imitation Learning for Bimanual Robotic Manipulation. - Lassi Meronen, Christabella Irwanto, Arno Solin:
Stationary Activations for Uncertainty Calibration in Deep Learning. - Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. - Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang:
Falcon: Fast Spectral Inference on Encrypted Data. - Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov:
On Power Laws in Deep Ensembles. - Donald Goldfarb, Yi Ren, Achraf Bahamou:
Practical Quasi-Newton Methods for Training Deep Neural Networks. - Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. - Jianfeng Zhang, Xuecheng Nie, Jiashi Feng:
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation. - Vu C. Dinh, Lam Si Tung Ho:
Consistent feature selection for analytic deep neural networks. - Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang:
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification. - Malik Boudiaf, Imtiaz Masud Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed:
Information Maximization for Few-Shot Learning. - Giorgia Ramponi, Gianluca Drappo, Marcello Restelli:
Inverse Reinforcement Learning from a Gradient-based Learner. - Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong:
Bayesian Multi-type Mean Field Multi-agent Imitation Learning. - Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. - Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, Yaron Lipman:
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance. - Emile Mathieu, Maximilian Nickel:
Riemannian Continuous Normalizing Flows. - Isabella Pozzi, Sander M. Bohté, Pieter R. Roelfsema:
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. - Ziv Goldfeld, Kristjan H. Greenewald, Kengo Kato:
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance. - Scott Pesme, Nicolas Flammarion:
Online Robust Regression via SGD on the l1 loss. - Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel:
PRANK: motion Prediction based on RANKing. - Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao:
Fighting Copycat Agents in Behavioral Cloning from Observation Histories. - Raphaël Berthier, Francis R. Bach, Pierre Gaillard:
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. - Ruohan Wang, Yiannis Demiris, Carlo Ciliberto:
Structured Prediction for Conditional Meta-Learning. - Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. - Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine:
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. - Lingkai Kong, Molei Tao:
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. - Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. - Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Emmanouil Zampetakis:
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions. - Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn:
Weakly-Supervised Reinforcement Learning for Controllable Behavior. - Duncan C. McElfresh, Michael J. Curry, Tuomas Sandholm, John Dickerson:
Improving Policy-Constrained Kidney Exchange via Pre-Screening. - Lucas Yanan Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum:
Learning abstract structure for drawing by efficient motor program induction. - Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. - Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj:
Dual Instrumental Variable Regression. - Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti:
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes. - Zhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua:
Interventional Few-Shot Learning. - Nan Jiang, Jiawei Huang:
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization. - Yifan Hu, Siqi Zhang, Xin Chen, Niao He:
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. - Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin:
ShiftAddNet: A Hardware-Inspired Deep Network. - Robin Rombach, Patrick Esser, Björn Ommer:
Network-to-Network Translation with Conditional Invertible Neural Networks. - Yash Savani, Colin White, Naveen Sundar Govindarajulu:
Intra-Processing Methods for Debiasing Neural Networks. - Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. - Jian Shen, Han Zhao, Weinan Zhang, Yong Yu:
Model-based Policy Optimization with Unsupervised Model Adaptation. - Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization and Convergence for Weight Normalization. - Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang:
Geometric All-way Boolean Tensor Decomposition. - Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. - Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu:
A/B Testing in Dense Large-Scale Networks: Design and Inference. - Vitaly Feldman, Chiyuan Zhang:
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. - Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng:
Partially View-aligned Clustering. - Laetitia Chapel, Mokhtar Z. Alaya, Gilles Gasso:
Partial Optimal Tranport with applications on Positive-Unlabeled Learning. - Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. - Laurent Orseau, Marcus Hutter, Omar Rivasplata:
Logarithmic Pruning is All You Need. - Guillermo Ortiz-Jiménez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard:
Hold me tight! Influence of discriminative features on deep network boundaries. - Raef Bassily, Shay Moran, Anupama Nandi:
Learning from Mixtures of Private and Public Populations. - Dongxian Wu, Shu-Tao Xia, Yisen Wang:
Adversarial Weight Perturbation Helps Robust Generalization. - Yuval Emek, Ron Lavi, Rad Niazadeh, Yangguang Shi:
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes. - Minseon Kim, Jihoon Tack, Sung Ju Hwang:
Adversarial Self-Supervised Contrastive Learning. - Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski:
Normalizing Kalman Filters for Multivariate Time Series Analysis. - Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano:
Learning to summarize with human feedback. - Tarik Dzanic, Karan Shah, Freddie D. Witherden:
Fourier Spectrum Discrepancies in Deep Network Generated Images. - Dongqi Han, Erik De Schutter, Sungho Hong:
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks. - Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L. Hamilton:
Learning Dynamic Belief Graphs to Generalize on Text-Based Games. - Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: where & why do they appear? - Raeid Saqur, Karthik Narasimhan:
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering. - Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. - Yulong Lu, Jianfeng Lu:
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions. - Paul Henderson, Christoph H. Lampert:
Unsupervised object-centric video generation and decomposition in 3D. - Haoliang Li, Yufei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, Alex C. Kot:
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization. - Guoqiang Wu, Jun Zhu:
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? - Dieqiao Feng, Carla P. Gomes, Bart Selman:
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances. - Atalanti-Anastasia Mastakouri, Bernhard Schölkopf:
Causal analysis of Covid-19 Spread in Germany. - Thomas Berrett, Cristina Butucea:
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms. - Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. - Solenne Gaucher:
Finite Continuum-Armed Bandits. - Itai Gat, Idan Schwartz, Alexander G. Schwing, Tamir Hazan:
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. - Severin Berger, Christian K. Machens:
Compact task representations as a normative model for higher-order brain activity. - Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov:
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. - Sijing Tu, Çigdem Aslay, Aristides Gionis:
Co-exposure Maximization in Online Social Networks. - Benoît Guillard, Edoardo Remelli, Pascal Fua:
UCLID-Net: Single View Reconstruction in Object Space. - Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim:
Reinforcement Learning for Control with Multiple Frequencies. - Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová:
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. - Naganand Yadati:
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs. - Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton:
A Unified View of Label Shift Estimation. - Christos Tzamos, Emmanouil V. Vlatakis-Gkaragkounis, Ilias Zadik:
Optimal Private Median Estimation under Minimal Distributional Assumptions. - Wei-Ning Chen, Peter Kairouz, Ayfer Özgür:
Breaking the Communication-Privacy-Accuracy Trilemma. - Kun Su, Xiulong Liu, Eli Shlizerman:
Audeo: Audio Generation for a Silent Performance Video. - Krzysztof Marcin Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani:
Ode to an ODE. - Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett:
Self-Distillation Amplifies Regularization in Hilbert Space. - Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. - Po-Wei Wang, J. Zico Kolter:
Community detection using fast low-cardinality semidefinite programming . - Jia Wan, Antoni B. Chan:
Modeling Noisy Annotations for Crowd Counting. - Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux:
An operator view of policy gradient methods. - Senthil Purushwalkam, Abhinav Gupta:
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases. - Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam:
Online MAP Inference of Determinantal Point Processes. - Yongqing Liang, Xin Li, Navid H. Jafari, Jim Chen:
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement. - Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow:
Inferring learning rules from animal decision-making. - Tuan Anh Nguyen, Anh Tuan Tran:
Input-Aware Dynamic Backdoor Attack. - Andreas Loukas:
How hard is to distinguish graphs with graph neural networks? - Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi:
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. - Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. - Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy:
Cross-Scale Internal Graph Neural Network for Image Super-Resolution. - Feng Wang, Huaping Liu, Di Guo, Fuchun Sun:
Unsupervised Representation Learning by Invariance Propagation. - Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li:
Restoring Negative Information in Few-Shot Object Detection. - Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry:
Do Adversarially Robust ImageNet Models Transfer Better? - Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck:
Robust Correction of Sampling Bias using Cumulative Distribution Functions. - Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. - Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander G. Hauptmann:
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation. - Yaniv Romano, Matteo Sesia, Emmanuel J. Candès:
Classification with Valid and Adaptive Coverage. - Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar:
Learning Global Transparent Models consistent with Local Contrastive Explanations. - Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis:
Learning to Approximate a Bregman Divergence. - Shweta Mahajan, Stefan Roth:
Diverse Image Captioning with Context-Object Split Latent Spaces. - Armand Comas Massague, Chi Zhang, Zlatan Feric, Octavia I. Camps, Rose Yu:
Learning Disentangled Representations of Videos with Missing Data. - Pim de Haan, Taco S. Cohen, Max Welling:
Natural Graph Networks. - Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon:
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. - Max Ryabinin, Anton Gusev:
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts. - Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou:
Bidirectional Convolutional Poisson Gamma Dynamical Systems. - Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm:
Deep Reinforcement and InfoMax Learning. - Clément Calauzènes, Nicolas Usunier:
On ranking via sorting by estimated expected utility. - Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas:
Distribution-free binary classification: prediction sets, confidence intervals and calibration. - Didrik Nielsen, Ole Winther:
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. - Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie:
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals. - Zhiyan Ding, Qin Li:
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo. - Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter F. Dominey, Pierre-Yves Oudeyer:
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration. - Sho Takase, Sosuke Kobayashi:
All Word Embeddings from One Embedding. - Adil Salim, Peter Richtárik:
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. - Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang:
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. - Richard Y. Zhang:
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples. - Arman Adibi, Aryan Mokhtari, Hamed Hassani:
Submodular Meta-Learning. - Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le:
Rethinking Pre-training and Self-training. - Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron J. Weiss, Kevin W. Wilson, John R. Hershey:
Unsupervised Sound Separation Using Mixture Invariant Training. - Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Lee Yu:
Adaptive Discretization for Model-Based Reinforcement Learning. - Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu:
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching. - Jordan T. Ash, Ryan P. Adams:
On Warm-Starting Neural Network Training. - Dennis Wei, Tian Gao, Yue Yu:
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. - Taewon Jeong, Heeyoung Kim:
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification. - Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone:
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch. - Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi:
Learning About Objects by Learning to Interact with Them. - Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer:
Learning discrete distributions with infinite support. - Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama:
Dissecting Neural ODEs. - Siddarth Asokan, Chandra Sekhar Seelamantula:
Teaching a GAN What Not to Learn. - Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. - Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Xiangyu Zhang, Hongbin Sun, Jian Sun, Nanning Zheng:
Rethinking Learnable Tree Filter for Generic Feature Transform. - Massimiliano Patacchiola, Amos J. Storkey:
Self-Supervised Relational Reasoning for Representation Learning. - Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong:
Sufficient dimension reduction for classification using principal optimal transport direction. - Jiajin Li, Caihua Chen, Anthony Man-Cho So:
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine. - Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Differentially Private Clustering: Tight Approximation Ratios. - Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic:
On the Power of Louvain in the Stochastic Block Model. - Forest Yang, Mouhamadou Cisse, Oluwasanmi Koyejo:
Fairness with Overlapping Groups; a Probabilistic Perspective. - Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva:
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. - Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing Xu, Chao Xu, Dacheng Tao, Chang Xu:
Searching for Low-Bit Weights in Quantized Neural Networks. - Qiong Wu, Felix Ming Fai Wong, Yanhua Li, Zhenming Liu, Varun Kanade:
Adaptive Reduced Rank Regression. - Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath, David C. Parkes:
From Predictions to Decisions: Using Lookahead Regularization. - Sue Zheng, David S. Hayden, Jason Pacheco, John W. Fisher III:
Sequential Bayesian Experimental Design with Variable Cost Structure. - Byol Kim, Chen Xu, Rina Foygel Barber:
Predictive inference is free with the jackknife+-after-bootstrap. - Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova:
Counterfactual Predictions under Runtime Confounding. - Ildoo Kim, Younghoon Kim, Sungwoong Kim:
Learning Loss for Test-Time Augmentation. - Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li:
Balanced Meta-Softmax for Long-Tailed Visual Recognition. - Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh:
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. - Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. - Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani B. Srivastava:
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods. - Kunal Talwar:
On the Error Resistance of Hinge-Loss Minimization. - Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. - Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov:
Object Goal Navigation using Goal-Oriented Semantic Exploration. - Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter:
Efficient semidefinite-programming-based inference for binary and multi-class MRFs. - Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le:
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. - Matthew Chang, Arjun Gupta, Saurabh Gupta:
Semantic Visual Navigation by Watching YouTube Videos. - Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Éric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin:
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation. - Thibault Castells, Philippe Weinzaepfel, Jérôme Revaud:
SuperLoss: A Generic Loss for Robust Curriculum Learning. - Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic:
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. - Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee:
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards. - Sebastian Farquhar, Lewis Smith, Yarin Gal:
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. - Tengyu Xu, Zhe Wang, Yingbin Liang:
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. - Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud:
Learning Differential Equations that are Easy to Solve. - Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. - Jinke He, Miguel Suau, Frans A. Oliehoek:
Influence-Augmented Online Planning for Complex Environments. - Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri:
PAC-Bayes Learning Bounds for Sample-Dependent Priors. - Hong Jun Jeon, Smitha Milli, Anca D. Dragan:
Reward-rational (implicit) choice: A unifying formalism for reward learning. - Vincent Le Guen, Nicolas Thome:
Probabilistic Time Series Forecasting with Shape and Temporal Diversity. - Sarah Jane Hong, Martín Arjovsky, Darryl Barnhart, Ian Thompson:
Low Distortion Block-Resampling with Spatially Stochastic Networks. - Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. - Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec:
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. - Lu Chi, Borui Jiang, Yadong Mu:
Fast Fourier Convolution. - Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron C. Courville:
Unsupervised Learning of Dense Visual Representations. - Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Higher-Order Certification For Randomized Smoothing. - Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. - Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes:
Hierarchical Quantized Autoencoders. - Yusuke Tashiro, Yang Song, Stefano Ermon:
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. - Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar:
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis. - Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan:
AvE: Assistance via Empowerment. - Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. - Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, International Brain Laboratory, Ila Fiete:
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice. - Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang:
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. - Marcin Tomczak, Siddharth Swaroop, Richard E. Turner:
Efficient Low Rank Gaussian Variational Inference for Neural Networks. - Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta:
Privacy Amplification via Random Check-Ins. - Andy Shih, Stefano Ermon:
Probabilistic Circuits for Variational Inference in Discrete Graphical Models. - Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, Venkatesh Babu R.:
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation. - Yuki Markus Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi:
Labelling unlabelled videos from scratch with multi-modal self-supervision. - Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. - Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh:
Robust Meta-learning for Mixed Linear Regression with Small Batches. - Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. - Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos:
Unsupervised Learning of Object Landmarks via Self-Training Correspondence. - Yue Li, Ilmun Kim, Yuting Wei:
Randomized tests for high-dimensional regression: A more efficient and powerful solution. - Pedro Morgado, Yi Li, Nuno Vasconcelos:
Learning Representations from Audio-Visual Spatial Alignment. - Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker:
Generative View Synthesis: From Single-view Semantics to Novel-view Images. - Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei:
Towards More Practical Adversarial Attacks on Graph Neural Networks. - Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang:
Multi-Task Reinforcement Learning with Soft Modularization. - Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. - Aitor Lewkowycz, Guy Gur-Ari:
On the training dynamics of deep networks with $L_2$ regularization. - Yuanhao Wang, Jian Li:
Improved Algorithms for Convex-Concave Minimax Optimization. - Jialin Yuan, Chao Chen, Fuxin Li:
Deep Variational Instance Segmentation. - Feng Liu, Xiaoming Liu:
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence. - Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang:
Deep Multimodal Fusion by Channel Exchanging. - Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer:
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems. - Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. - Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Delay and Cooperation in Nonstochastic Linear Bandits. - David Mohlin, Josephine Sullivan, Gérald Bianchi:
Probabilistic Orientation Estimation with Matrix Fisher Distributions. - Qianyi Li, Cengiz Pehlevan:
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons. - Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. - Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu:
Towards Deeper Graph Neural Networks with Differentiable Group Normalization. - Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic, Moritz Hardt:
Stochastic Optimization for Performative Prediction. - Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri:
Learning Differentiable Programs with Admissible Neural Heuristics. - Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. - Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. - Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and size of the weights in memorization with two-layers neural networks. - Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson:
Certifying Strategyproof Auction Networks. - Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine:
Continual Learning of Control Primitives : Skill Discovery via Reset-Games. - Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu:
HOI Analysis: Integrating and Decomposing Human-Object Interaction. - Meng Liu, David F. Gleich:
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering. - Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath:
Deep Direct Likelihood Knockoffs. - Siyuan Shan, Yang Li, Junier B. Oliva:
Meta-Neighborhoods. - Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak:
Neural Dynamic Policies for End-to-End Sensorimotor Learning. - Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora:
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons. - Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. - Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang:
Attribution Preservation in Network Compression for Reliable Network Interpretation. - Maksymilian Wojtas, Ke Chen:
Feature Importance Ranking for Deep Learning. - Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
Causal Estimation with Functional Confounders. - Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. - Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu:
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. - Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. - Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. - Shuqi Yang, Xingzhe He, Bo Zhu:
Learning Physical Constraints with Neural Projections. - Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. - Hongyuan Mei, Tom Wan, Jason Eisner:
Noise-Contrastive Estimation for Multivariate Point Processes. - Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei Wang, Xiang Yan:
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling. - Chandrashekar Lakshminarayanan, Amit Vikram Singh:
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning. - Shaojie Bai, Vladlen Koltun, J. Zico Kolter:
Multiscale Deep Equilibrium Models. - Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak:
Sparse Graphical Memory for Robust Planning. - Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin:
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. - Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang:
Dirichlet Graph Variational Autoencoder. - Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell:
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. - Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton van den Hengel:
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen. - Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex M. Bronstein, Uri C. Weiser:
Robust Quantization: One Model to Rule Them All. - Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli:
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. - Honglin Yuan, Tengyu Ma:
Federated Accelerated Stochastic Gradient Descent. - Ananya Uppal, Shashank Singh, Barnabás Póczos:
Robust Density Estimation under Besov IPM Losses. - Franco Pellegrini, Giulio Biroli:
An analytic theory of shallow networks dynamics for hinge loss classification. - Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. - Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano:
Learning to Orient Surfaces by Self-supervised Spherical CNNs. - Rui Liu, Tianyi Wu, Barzan Mozafari:
Adam with Bandit Sampling for Deep Learning. - Maximus Mutschler, Andreas Zell:
Parabolic Approximation Line Search for DNNs. - Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. - Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet:
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. - Yossi Arjevani, Michael Field:
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry. - Matthew R. O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark A. Davenport:
Generative causal explanations of black-box classifiers. - Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard:
Sub-sampling for Efficient Non-Parametric Bandit Exploration. - Andrés R. Masegosa:
Learning under Model Misspecification: Applications to Variational and Ensemble methods. - Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. - Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Hai Li:
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles. - Lun Wang, Qi Pang, Dawn Song:
Towards practical differentially private causal graph discovery. - Constantinos Daskalakis, Dylan J. Foster, Noah Golowich:
Independent Policy Gradient Methods for Competitive Reinforcement Learning. - Christopher Grimm, André Barreto, Satinder Singh, David Silver:
The Value Equivalence Principle for Model-Based Reinforcement Learning. - Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli:
Structured Convolutions for Efficient Neural Network Design. - Aleksandr Ermolov, Nicu Sebe:
Latent World Models For Intrinsically Motivated Exploration. - Jingqiu Ding, Samuel B. Hopkins, David Steurer:
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks. - Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement via Imitation of Multiple Oracles. - Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray:
Learning of Discrete Graphical Models with Neural Networks. - Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu:
RepPoints v2: Verification Meets Regression for Object Detection. - Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan:
Unfolding the Alternating Optimization for Blind Super Resolution. - Vasileios Charisopoulos, Austin R. Benson, Anil Damle:
Entrywise convergence of iterative methods for eigenproblems. - Nanbo Li, Cian Eastwood, Robert B. Fisher:
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views. - Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He:
A Catalyst Framework for Minimax Optimization. - Tengda Han, Weidi Xie, Andrew Zisserman:
Self-supervised Co-Training for Video Representation Learning. - Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. - Janarthanan Rajendran, Alexander Irpan, Eric Jang:
Meta-Learning Requires Meta-Augmentation. - Paria Rashidinejad, Jiantao Jiao, Stuart Russell:
SLIP: Learning to predict in unknown dynamical systems with long-term memory. - Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. - Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling:
Bayesian Bits: Unifying Quantization and Pruning. - Kuldeep S. Meel, Yash Pote, Sourav Chakraborty:
On Testing of Samplers. - Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood:
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. - Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou:
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. - Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. - You Lu, Bert Huang:
Woodbury Transformations for Deep Generative Flows. - Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen:
Graph Contrastive Learning with Augmentations. - Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. - Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. - Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. - Kexin Huang, Marinka Zitnik:
Graph Meta Learning via Local Subgraphs. - Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia:
Stochastic Deep Gaussian Processes over Graphs. - Junsouk Choi, Robert S. Chapkin, Yang Ni:
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks. - Benjamín Sánchez-Lengeling, Jennifer N. Wei, Brian K. Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy J. Colwell, Alexander B. Wiltschko:
Evaluating Attribution for Graph Neural Networks. - Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió:
On Second Order Behaviour in Augmented Neural ODEs. - Amirata Ghorbani, James Y. Zou:
Neuron Shapley: Discovering the Responsible Neurons. - Hao Wu, Jonas Köhler, Frank Noé:
Stochastic Normalizing Flows. - John T. Halloran, David M. Rocke:
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification. - Christopher De Sa:
Random Reshuffling is Not Always Better. - Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar:
Model Agnostic Multilevel Explanations. - Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
NeuMiss networks: differentiable programming for supervised learning with missing values. - Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang, Irwin King, Michael R. Lyu, Jian Cheng:
Revisiting Parameter Sharing for Automatic Neural Channel Number Search. - Abhimanyu Dubey, Alex 'Sandy' Pentland:
Differentially-Private Federated Linear Bandits. - Qiwen Cui, Lin F. Yang:
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? - Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. K. Yamins:
Learning Physical Graph Representations from Visual Scenes. - Anqi Wu, Estefany Kelly Buchanan, Matthew R. Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora E. Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski:
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. - Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning from Tasks with Heterogeneous Attribute Spaces. - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Estimating decision tree learnability with polylogarithmic sample complexity. - Daniel M. DiPietro, Shiying Xiong, Bo Zhu:
Sparse Symplectically Integrated Neural Networks. - Nicolai Häni, Selim Engin, Jun-Jee Chao, Volkan Isler:
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision. - Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt:
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. - Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey:
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. - Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang:
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. - Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto:
Predicting Training Time Without Training. - Michael Tsang, Sirisha Rambhatla, Yan Liu:
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions. - John S. Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran:
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. - Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri:
Neurosymbolic Reinforcement Learning with Formally Verified Exploration. - Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker:
Wavelet Flow: Fast Training of High Resolution Normalizing Flows. - Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik I. Christensen, Hao Su:
Multi-task Batch Reinforcement Learning with Metric Learning. - Josue Nassar, Piotr A. Sokól, SueYeon Chung, Kenneth D. Harris, Il Memming Park:
On 1/n neural representation and robustness. - Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. - Yu Takagi, Steven W. Kennerley, Jun-ichiro Hirayama, Laurence T. Hunt:
Demixed shared component analysis of neural population data from multiple brain areas. - Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. - Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, Quoc Le:
Unsupervised Data Augmentation for Consistency Training. - Yueming Lyu, Yuan Yuan, Ivor W. Tsang:
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. - Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. - Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González:
Multi-task Causal Learning with Gaussian Processes. - Joong-Ho Won:
Proximity Operator of the Matrix Perspective Function and its Applications. - Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong:
Generative 3D Part Assembly via Dynamic Graph Learning. - Ekta Sood, Simon Tannert, Philipp Müller, Andreas Bulling:
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention. - Max Hopkins, Daniel Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. - Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. - Kwang-Sung Jun, Chicheng Zhang:
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. - Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. - Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu:
Detecting Interactions from Neural Networks via Topological Analysis. - Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi:
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. - Rohan R. Paleja, Andrew Silva, Letian Chen, Matthew C. Gombolay:
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations. - Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao:
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. - Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. - Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft, Cecilia Mascolo:
Federated Principal Component Analysis. - Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. - Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis:
SMYRF - Efficient Attention using Asymmetric Clustering. - Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos D. Kollias:
Introducing Routing Uncertainty in Capsule Networks. - Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin:
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. - Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. - Bailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D. Procaccia:
Neutralizing Self-Selection Bias in Sampling for Sortition. - Elena Smirnova, Elvis Dohmatob:
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes. - Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans:
Off-Policy Evaluation via the Regularized Lagrangian. - Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar:
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. - Niklas Heim, Tomás Pevný, Václav Smídl:
Neural Power Units. - Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. - Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu:
A Dictionary Approach to Domain-Invariant Learning in Deep Networks. - Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping neural processes. - Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu:
Large-Scale Adversarial Training for Vision-and-Language Representation Learning. - Amit Daniely, Hadas Shacham:
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. - Yilun Du, Shuang Li, Igor Mordatch:
Compositional Visual Generation with Energy Based Models. - David Chiang, Darcey Riley:
Factor Graph Grammars. - Nikolaos Karalias, Andreas Loukas:
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. - Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. - Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. - Patrick Kidger, James Morrill, James Foster, Terry J. Lyons:
Neural Controlled Differential Equations for Irregular Time Series. - Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh:
On Efficiency in Hierarchical Reinforcement Learning. - Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
On Correctness of Automatic Differentiation for Non-Differentiable Functions. - Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. - Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie:
Dynamic Regret of Policy Optimization in Non-Stationary Environments. - Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. - Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy J. Mitra:
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images. - Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, Caiming Xiong:
Online Structured Meta-learning. - Rakshit Trivedi, Hongyuan Zha:
Learning Strategic Network Emergence Games. - Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang:
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. - Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant:
The Mean-Squared Error of Double Q-Learning. - Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola:
What Makes for Good Views for Contrastive Learning? - Jonathan Ho, Ajay Jain, Pieter Abbeel:
Denoising Diffusion Probabilistic Models. - John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. - Lijia Zhou, Danica J. Sutherland, Nati Srebro:
On Uniform Convergence and Low-Norm Interpolation Learning. - Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi:
Bandit Samplers for Training Graph Neural Networks. - Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. - Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. - Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang:
Hierarchical Poset Decoding for Compositional Generalization in Language. - Tom Yan, Christian Kroer, Alexander Peysakhovich:
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions. - Yang Li, Haidong Yi, Christopher M. Bender, Siyuan Shan, Junier B. Oliva:
Exchangeable Neural ODE for Set Modeling. - Yi Hao, Alon Orlitsky:
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions. - Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao:
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection. - Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse:
Regularized linear autoencoders recover the principal components, eventually. - Wei Wang, Min-Ling Zhang:
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. - Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel:
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars. - Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang:
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection. - Guy Bresler, Rares-Darius Buhai:
Learning Restricted Boltzmann Machines with Sparse Latent Variables. - Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. - Adel Nabli, Margarida Carvalho:
Curriculum learning for multilevel budgeted combinatorial problems. - Reese Pathak, Martin J. Wainwright:
FedSplit: an algorithmic framework for fast federated optimization. - Aude Sportisse, Claire Boyer, Julie Josse:
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data. - Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan:
Correlation Robust Influence Maximization. - Johannes Friedrich:
Neuronal Gaussian Process Regression. - Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. - Olivier Bousquet, Roi Livni, Shay Moran:
Synthetic Data Generators - Sequential and Private. - Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie:
Uncertainty Quantification for Inferring Hawkes Networks. - Yuguang Yue, Zhendong Wang, Mingyuan Zhou:
Implicit Distributional Reinforcement Learning. - Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu:
Auxiliary Task Reweighting for Minimum-data Learning. - Brian Hu Zhang, Tuomas Sandholm:
Small Nash Equilibrium Certificates in Very Large Games. - Arash Ardakani, Amir Ardakani, Warren J. Gross:
Training Linear Finite-State Machines. - Chicheng Zhang, Jie Shen, Pranjal Awasthi:
Efficient active learning of sparse halfspaces with arbitrary bounded noise. - Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang:
Swapping Autoencoder for Deep Image Manipulation. - Charu Sharma, Manohar Kaul:
Self-Supervised Few-Shot Learning on Point Clouds. - Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. - Ding Zhou, Xue-Xin Wei:
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE. - Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. - Jayanta Mandi, Tias Guns:
Interior Point Solving for LP-based prediction+optimisation. - Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. - Roman Pogodin, Peter E. Latham:
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. - Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh:
Understanding the Role of Training Regimes in Continual Learning. - Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression with Wasserstein barycenters. - Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang:
Training Stronger Baselines for Learning to Optimize. - Matt Jordan, Alexandros G. Dimakis:
Exactly Computing the Local Lipschitz Constant of ReLU Networks. - Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Strictly Batch Imitation Learning by Energy-based Distribution Matching. - Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu:
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. - Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhi-Quan Luo:
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems. - Niv Pekar, Yaniv Benny, Lior Wolf:
Generating Correct Answers for Progressive Matrices Intelligence Tests. - Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk:
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss. - Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright:
Preference learning along multiple criteria: A game-theoretic perspective. - Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu:
Multi-Plane Program Induction with 3D Box Priors. - Anne Draelos, John M. Pearson:
Online Neural Connectivity Estimation with Noisy Group Testing. - Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang:
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. - Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein:
Implicit Neural Representations with Periodic Activation Functions. - Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin:
Rotated Binary Neural Network. - Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. - Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. - Govinda M. Kamath, Tavor Z. Baharav, Ilan Shomorony:
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment. - Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio:
Hierarchical nucleation in deep neural networks. - Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng:
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. - Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang:
Graph Geometry Interaction Learning. - Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han:
Differentiable Augmentation for Data-Efficient GAN Training. - Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang:
Heuristic Domain Adaptation. - Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. - Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. - Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor:
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. - Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin:
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods. - Orestis Plevrakis, Elad Hazan:
Geometric Exploration for Online Control. - Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto:
Automatic Curriculum Learning through Value Disagreement. - Aaron Defazio, Tullie Murrell, Michael P. Recht:
MRI Banding Removal via Adversarial Training. - Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel:
The NetHack Learning Environment. - Yicong Hong, Cristian Rodriguez Opazo, Yuankai Qi, Qi Wu, Stephen Gould:
Language and Visual Entity Relationship Graph for Agent Navigation. - Cher Bass, Mariana da Silva, Carole H. Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson:
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping. - Zhou Fan, Zhichao Wang:
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks. - Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti:
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium. - Andreas Maurer, Massimiliano Pontil:
Estimating weighted areas under the ROC curve. - Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. - Alexander C. Li, Lerrel Pinto, Pieter Abbeel:
Generalized Hindsight for Reinforcement Learning. - Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas:
Critic Regularized Regression. - Tianyu Pang, Xiao Yang, Yinpeng Dong, Taufik Xu, Jun Zhu, Hang Su:
Boosting Adversarial Training with Hypersphere Embedding. - Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra:
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. - Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas M. Lehrmann:
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows. - Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis:
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent. - Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe:
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification. - Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai Nguyen:
Detecting Hands and Recognizing Physical Contact in the Wild. - Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. - Vrettos Moulos:
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards. - Yuki Kawana, Yusuke Mukuta, Tatsuya Harada:
Neural Star Domain as Primitive Representation. - Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu:
Off-Policy Interval Estimation with Lipschitz Value Iteration. - Minhae Kwon, Saurabh Daptardar, Paul R. Schrater, Zachary Pitkow:
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics. - Balthazar Donon, Zhengying Liu, Wenzhuo Liu, Isabelle Guyon, Antoine Marot, Marc Schoenauer:
Deep Statistical Solvers. - Viet Anh Nguyen, Xuhui Zhang, José H. Blanchet, Angelos Georghiou:
Distributionally Robust Parametric Maximum Likelihood Estimation. - Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, Pavel Kolev:
Secretary and Online Matching Problems with Machine Learned Advice. - Tom Monnier, Thibault Groueix, Mathieu Aubry:
Deep Transformation-Invariant Clustering. - Peizhong Ju, Xiaojun Lin, Jia Liu:
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree. - Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng:
Improving Generalization in Reinforcement Learning with Mixture Regularization. - Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou:
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework. - Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama:
Learning from Aggregate Observations. - Yingxiang Yang, Negar Kiyavash, Le Song, Niao He:
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. - Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik:
Subgraph Neural Networks. - Han Lin, Haoxian Chen, Krzysztof Marcin Choromanski, Tianyi Zhang, Clement Laroche:
Demystifying Orthogonal Monte Carlo and Beyond. - Alexander Wei, Fred Zhang:
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms. - Jinhyun So, Basak Güler, Salman Avestimehr:
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning. - Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon:
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search. - Behnam Neyshabur:
Towards Learning Convolutions from Scratch. - Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami:
Cycle-Contrast for Self-Supervised Video Representation Learning. - Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira:
Posterior Re-calibration for Imbalanced Datasets. - Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau:
Novelty Search in Representational Space for Sample Efficient Exploration. - Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, Volkan Cevher:
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics. - Nick Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh:
Adversarial Blocking Bandits. - Shufan Wang, Jian Li, Shiqiang Wang:
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice. - Jiaming Song, Stefano Ermon:
Multi-label Contrastive Predictive Coding. - Seohyun Kim, Jaeyoo Park, Bohyung Han:
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud. - Nuri Cingillioglu, Alessandra Russo:
Learning Invariants through Soft Unification. - Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn:
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. - Luigi Acerbi:
Variational Bayesian Monte Carlo with Noisy Likelihoods. - Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam:
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. - Chong Yu, Jeff Pool:
Self-Supervised Generative Adversarial Compression. - Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham:
An efficient nonconvex reformulation of stagewise convex optimization problems. - Anand Kalvit, Assaf Zeevi:
From Finite to Countable-Armed Bandits. - Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su:
Adversarial Distributional Training for Robust Deep Learning. - Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner:
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. - Pan Zhou, Caiming Xiong, Richard Socher, Steven Chu-Hong Hoi:
Theory-Inspired Path-Regularized Differential Network Architecture Search. - John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye:
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices. - Konik Kothari, Maarten V. de Hoop, Ivan Dokmanic:
Learning the Geometry of Wave-Based Imaging. - Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef M. Marzouk:
Greedy inference with structure-exploiting lazy maps. - Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun:
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. - Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha:
Finding the Homology of Decision Boundaries with Active Learning. - Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang:
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars. - Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo R. Jovanovic:
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes. - Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. - Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certified Defense to Image Transformations via Randomized Smoothing. - Ali Jadbabaie, Anuran Makur, Devavrat Shah:
Estimation of Skill Distribution from a Tournament. - Ehsan Amid, Manfred K. Warmuth:
Reparameterizing Mirror Descent as Gradient Descent. - Aahlad Manas Puli, Rajesh Ranganath:
General Control Functions for Causal Effect Estimation from IVs. - Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh:
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards. - Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang:
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks. - Linxiao Li, Can Xu, Wei Wu, Yufan Zhao, Xueliang Zhao, Chongyang Tao:
Zero-Resource Knowledge-Grounded Dialogue Generation. - Alex Wong, Safa Cicek, Stefano Soatto:
Targeted Adversarial Perturbations for Monocular Depth Prediction. - Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch:
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. - Shengyi Jiang, Jing-Cheng Pang, Yang Yu:
Offline Imitation Learning with a Misspecified Simulator. - Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. - Henry Charlesworth, Giovanni Montana:
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals. - Shengchao Liu, Dimitris S. Papailiopoulos, Dimitris Achlioptas:
Bad Global Minima Exist and SGD Can Reach Them. - Yi Hao, Ping Li:
Optimal Prediction of the Number of Unseen Species with Multiplicity. - Sanghack Lee, Elias Bareinboim:
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. - Zhen Zhang, Fan Wu, Wee Sun Lee:
Factor Graph Neural Networks. - Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri:
A Closer Look at Accuracy vs. Robustness. - Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes:
Curriculum Learning by Dynamic Instance Hardness. - Carlos Esteves, Ameesh Makadia, Kostas Daniilidis:
Spin-Weighted Spherical CNNs. - David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow:
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. - Qian Lou, Song Bian, Lei Jiang:
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference. - Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Baxter Permutation Process. - Yinan Cao, Christopher Summerfield, Andrew M. Saxe:
Characterizing emergent representations in a space of candidate learning rules for deep networks. - Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. - Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala:
Adaptive Probing Policies for Shortest Path Routing. - Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Lutong Wang, Wenshuo Guo:
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. - Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas:
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. - Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam:
Sliding Window Algorithms for k-Clustering Problems. - Ximeng Sun, Rameswar Panda, Rogério Feris, Kate Saenko:
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning. - Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick:
Approximate Cross-Validation for Structured Models. - Sajad Norouzi, David J. Fleet, Mohammad Norouzi:
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation. - Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka:
Debiased Contrastive Learning. - Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz:
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree. - Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar:
Generalized Boosting. - Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio:
COT-GAN: Generating Sequential Data via Causal Optimal Transport. - Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann:
Impossibility Results for Grammar-Compressed Linear Algebra. - Allan Mancoo, Sander W. Keemink, Christian K. Machens:
Understanding spiking networks through convex optimization. - Ashok Cutkosky:
Better Full-Matrix Regret via Parameter-Free Online Learning. - Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. - Taira Tsuchiya, Junya Honda, Masashi Sugiyama:
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. - Asaf B. Cassel, Tomer Koren:
Bandit Linear Control. - Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su:
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals. - Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William M. Wells III:
PEP: Parameter Ensembling by Perturbation. - Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi:
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. - Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton:
Adversarial Example Games. - Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei Zhang, Jiashi Feng, Tong Zhang:
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts. - Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang:
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach. - Pinar Ozisik, Philip S. Thomas:
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms. - Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause:
Learning to Play Sequential Games versus Unknown Opponents. - Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. - Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang:
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning. - Amit Daniely:
Neural Networks Learning and Memorization with (almost) no Over-Parameterization. - Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov:
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. - Alon Gonen, Shachar Lovett, Michal Moshkovitz:
Towards a Combinatorial Characterization of Bounded-Memory Learning. - Yun Kuen Cheung, Georgios Piliouras:
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games. - Yinglun Zhu, Robert Nowak:
On Regret with Multiple Best Arms. - Adel M. Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer:
Matrix Completion with Hierarchical Graph Side Information. - Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade:
Is Long Horizon RL More Difficult Than Short Horizon RL? - Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal:
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. - Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu:
Adversarial Learning for Robust Deep Clustering. - Brian Hie, Ellen D. Zhong, Bryan Bryson, Bonnie Berger:
Learning Mutational Semantics. - Xiantong Zhen, Ying-Jun Du, Huan Xiong, Qiang Qiu, Cees Snoek, Ling Shao:
Learning to Learn Variational Semantic Memory. - Allen Liu, Renato Paes Leme, Jon Schneider:
Myersonian Regression. - Kaifu Wang, Qiang Ning, Dan Roth:
Learnability with Indirect Supervision Signals. - Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas:
Towards Safe Policy Improvement for Non-Stationary MDPs. - Simon Foucart, David Koslicki:
Finer Metagenomic Reconstruction via Biodiversity Optimization. - Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. - Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang:
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data. - Nika Haghtalab, Tim Roughgarden, Abhishek Shetty:
Smoothed Analysis of Online and Differentially Private Learning. - Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. - Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan:
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction. - Jay Nandy, Wynne Hsu, Mong-Li Lee:
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples. - Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason M. Saragih, Hao Li, Yaser Sheikh:
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels. - Xiang Zhang, Marinka Zitnik:
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. - Tong He, John P. Collomosse, Hailin Jin, Stefano Soatto:
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction. - Shima Rashidi, Krista A. Ehinger, Andrew Turpin, Lars Kulik:
Optimal visual search based on a model of target detectability in natural images. - Wei Gao, Zhi-Hua Zhou:
Towards Convergence Rate Analysis of Random Forests for Classification. - Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. - Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice:
Exact Recovery of Mangled Clusters with Same-Cluster Queries. - Bojun Huang:
Steady State Analysis of Episodic Reinforcement Learning. - Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala:
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. - Vu Nguyen, Sebastian Schulze, Michael A. Osborne:
Bayesian Optimization for Iterative Learning. - Kuan-Yun Lee, Thomas A. Courtade:
Minimax Bounds for Generalized Linear Models. - Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. - Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. - Xiaocheng Li, Chunlin Sun, Yinyu Ye:
Simple and Fast Algorithm for Binary Integer and Online Linear Programming. - Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma:
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction. - Arjun Seshadri, Stephen Ragain, Johan Ugander:
Learning Rich Rankings. - Elad Hirsch, Ayellet Tal:
Color Visual Illusions: A Statistics-based Computational Model. - Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela:
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. - Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan:
Universal guarantees for decision tree induction via a higher-order splitting criterion. - Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon:
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation. - Geraud Nangue Tasse, Steven James, Benjamin Rosman:
A Boolean Task Algebra for Reinforcement Learning. - Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach:
Learning with Differentiable Pertubed Optimizers. - Nick Bishop, Long Tran-Thanh, Enrico H. Gerding:
Optimal Learning from Verified Training Data. - Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Linear Optimization with Many Hints. - Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová:
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. - Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. - Dirk van der Hoeven:
Exploiting the Surrogate Gap in Online Multiclass Classification. - Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. - Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe:
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems. - Nikos Karampatziakis, John Langford, Paul Mineiro:
Empirical Likelihood for Contextual Bandits. - Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro:
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? - Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin:
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. - Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng:
Listening to Sounds of Silence for Speech Denoising. - Ralph Abboud, Ismail Ilkan Ceylan, Thomas Lukasiewicz, Tommaso Salvatori:
BoxE: A Box Embedding Model for Knowledge Base Completion. - Eleonora Giunchiglia, Thomas Lukasiewicz:
Coherent Hierarchical Multi-Label Classification Networks. - Simone Rossi, Sébastien Marmin, Maurizio Filippone:
Walsh-Hadamard Variational Inference for Bayesian Deep Learning. - Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. - Saim Wani, Shivansh Patel, Unnat Jain, Angel X. Chang, Manolis Savva:
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation. - Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi:
Neural Complexity Measures. - Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci:
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. - Tongyi Cao, Akshay Krishnamurthy:
Provably adaptive reinforcement learning in metric spaces. - Chiyu Max Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas:
ShapeFlow: Learnable Deformation Flows Among 3D Shapes. - Humam Alwassel, Dhruv Mahajan, Bruno Korbar, Lorenzo Torresani, Bernard Ghanem, Du Tran:
Self-Supervised Learning by Cross-Modal Audio-Video Clustering. - Wei Tang, Chien-Ju Ho, Yang Liu:
Optimal Query Complexity of Secure Stochastic Convex Optimization. - Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu:
DynaBERT: Dynamic BERT with Adaptive Width and Depth. - Mingzhi Dong, Xiaochen Yang, Rui Zhu, Yujiang Wang, Jing-Hao Xue:
Generalization Bound of Gradient Descent for Non-Convex Metric Learning. - Morteza Monemizadeh:
Dynamic Submodular Maximization. - Kelly W. Zhang, Lucas Janson, Susan A. Murphy:
Inference for Batched Bandits. - William T. Stephenson, Madeleine Udell, Tamara Broderick:
Approximate Cross-Validation with Low-Rank Data in High Dimensions. - Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris:
GANSpace: Discovering Interpretable GAN Controls. - Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. - Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, Xiang Li:
Neuron-level Structured Pruning using Polarization Regularizer. - Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama:
Limits on Testing Structural Changes in Ising Models. - Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu:
Field-wise Learning for Multi-field Categorical Data. - Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr:
Continual Learning in Low-rank Orthogonal Subspaces. - Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin:
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. - Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite:
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. - Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, Sanja Fidler:
Learning Deformable Tetrahedral Meshes for 3D Reconstruction. - Clément Luneau, Jean Barbier, Nicolas Macris:
Information theoretic limits of learning a sparse rule. - A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake:
Self-supervised learning through the eyes of a child. - Tao Han, Junyu Gao, Yuan Yuan, Qi Wang:
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning. - Arnu Pretorius, Scott Alexander Cameron, Elan Van Biljon, Tom Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan P. Shock, Alexandre Laterre, Karim Beguir:
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning. - Katherine L. Hermann, Andrew K. Lampinen:
What shapes feature representations? Exploring datasets, architectures, and training. - Yassir Jedra, Alexandre Proutière:
Optimal Best-arm Identification in Linear Bandits. - Xuan-Phi Nguyen, Shafiq R. Joty, Kui Wu, Ai Ti Aw:
Data Diversification: A Simple Strategy For Neural Machine Translation. - Yongqi Zhang, Quanming Yao, Lei Chen:
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. - Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges:
CoSE: Compositional Stroke Embeddings. - Jing Xu, Fangwei Zhong, Yizhou Wang:
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks. - William F. Podlaski, Christian K. Machens:
Biological credit assignment through dynamic inversion of feedforward networks. - Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou:
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching. - Murtaza Rangwala, Ryan Williams:
Learning Multi-Agent Communication through Structured Attentive Reasoning. - Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. - Denny Wu, Ji Xu:
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. - Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon:
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search. - Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein:
MetaSDF: Meta-Learning Signed Distance Functions. - Zhiyue Zhang, Kenneth Lange, Jason Xu:
Simple and Scalable Sparse k-means Clustering via Feature Ranking. - Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma:
Model-based Adversarial Meta-Reinforcement Learning. - Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang:
Graph Policy Network for Transferable Active Learning on Graphs. - Ruoyu Sun, Tiantian Fang, Alexander G. Schwing:
Towards a Better Global Loss Landscape of GANs. - Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson:
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony:
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits. - Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, In So Kweon:
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging. - Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone:
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders. - Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, Zhi-Hua Zhou:
An Unbiased Risk Estimator for Learning with Augmented Classes. - Yikang Zhang, Jian Zhang, Zhao Zhong:
AutoBSS: An Efficient Algorithm for Block Stacking Style Search. - Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov, Anna Vinogradsky, Sarah Massengill, Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka, Xia Song, Subhojit Som, Kaustav Das, Saurabh Tiwary, Steven K. Reinhardt, Sitaram Lanka, Eric S. Chung, Doug Burger:
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point. - Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. - Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim:
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs. - Zhuokun Yao, Kun Li, Ying Fu, Haofeng Hu, Boxin Shi:
GPS-Net: Graph-based Photometric Stereo Network. - Peike Li, Yunchao Wei, Yi Yang:
Consistent Structural Relation Learning for Zero-Shot Segmentation. - Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. - Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Truncated Linear Regression in High Dimensions. - Yipeng Kang, Tonghan Wang, Gerard de Melo:
Incorporating Pragmatic Reasoning Communication into Emergent Language. - Mahdi Abavisani, Alireza Naghizadeh, Dimitris N. Metaxas, Vishal M. Patel:
Deep Subspace Clustering with Data Augmentation. - Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin G. Jamieson:
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. - Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna:
Can Graph Neural Networks Count Substructures? - Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. - Tomer Galanti, Lior Wolf:
On the Modularity of Hypernetworks. - Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. - Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong:
Provably Efficient Neural GTD for Off-Policy Learning. - Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. - Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex G. Liu, Thomas Serre:
Stable and expressive recurrent vision models. - Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi:
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form. - Lukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, Nicholas D. Lane:
BRP-NAS: Prediction-based NAS using GCNs. - Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers:
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. - Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, Zhouchen Lin:
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding. - Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng:
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D. - Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. - Feiyang Pan, Jia He, Dandan Tu, Qing He:
Trust the Model When It Is Confident: Masked Model-based Actor-Critic. - Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu:
Semi-Supervised Neural Architecture Search. - Jongheon Jeong, Jinwoo Shin:
Consistency Regularization for Certified Robustness of Smoothed Classifiers. - Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar:
Robust Multi-Agent Reinforcement Learning with Model Uncertainty. - Peiyao Wang, Weixin Luo, Yanyu Xu, Haojie Li, Shugong Xu, Jianyu Yang, Shenghua Gao:
SIRI: Spatial Relation Induced Network For Spatial Description Resolution. - Nesreen K. Ahmed, Nick Duffield:
Adaptive Shrinkage Estimation for Streaming Graphs. - Tim Meinhardt, Laura Leal-Taixé:
Make One-Shot Video Object Segmentation Efficient Again. - Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato:
Depth Uncertainty in Neural Networks. - Anastasis Kratsios, Ievgen Bilokopytov:
Non-Euclidean Universal Approximation. - Jacob Deasy, Nikola Simidjievski, Pietro Lió:
Constraining Variational Inference with Geometric Jensen-Shannon Divergence. - Peter M. C. Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby:
Gibbs Sampling with People. - Jie Ren, Minjia Zhang, Dong Li:
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory. - Lingjiao Chen, Matei Zaharia, James Y. Zou:
FrugalML: How to use ML Prediction APIs more accurately and cheaply. - Guy Bresler, Dheeraj Nagaraj:
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth. - Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht:
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning. - Ezra Winston, J. Zico Kolter:
Monotone operator equilibrium networks. - Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes. - Yaofeng Desmond Zhong, Naomi Ehrich Leonard:
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control. - Botao Hao, Tor Lattimore, Mengdi Wang:
High-Dimensional Sparse Linear Bandits. - Paula Gradu, John Hallman, Elad Hazan:
Non-Stochastic Control with Bandit Feedback. - Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu:
Generalized Leverage Score Sampling for Neural Networks. - Avinatan Hassidim, Ron Kupfer, Yaron Singer:
An Optimal Elimination Algorithm for Learning a Best Arm. - Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu:
Efficient Projection-free Algorithms for Saddle Point Problems. - Jérôme Bolte, Edouard Pauwels:
A mathematical model for automatic differentiation in machine learning. - Jingjing Li, Zichao Li, Lili Mou, Xin Jiang, Michael R. Lyu, Irwin King:
Unsupervised Text Generation by Learning from Search. - Maxwell I. Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M. Lake:
Learning Compositional Rules via Neural Program Synthesis. - Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen, Enhong Chen:
Incorporating BERT into Parallel Sequence Decoding with Adapters. - Sahand Farhoodi, Mark Plitt, Lisa M. Giocomo, Uri T. Eden:
Estimating Fluctuations in Neural Representations of Uncertain Environments. - KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon:
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation. - Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar:
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm. - Ryo Karakida, Kazuki Osawa:
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. - Juan D. Correa, Elias Bareinboim:
General Transportability of Soft Interventions: Completeness Results. - Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni:
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. - Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti:
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing. - Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, Chang Xu:
SCOP: Scientific Control for Reliable Neural Network Pruning. - Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. - Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. - Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis:
Discovering conflicting groups in signed networks. - Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra:
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. - Victor Veitch, Anisha Zaveri:
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. - Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai:
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. - Ben Adlam, Jeffrey Pennington:
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. - Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar:
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. - Lirong Xia:
The Smoothed Possibility of Social Choice. - Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das:
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. - Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
Phase retrieval in high dimensions: Statistical and computational phase transitions. - Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo:
Fair Performance Metric Elicitation. - Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen:
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. - Genevieve Flaspohler, Nicholas A. Roy, John W. Fisher III:
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information. - Janine Thoma, Danda Pani Paudel, Luc Van Gool:
Soft Contrastive Learning for Visual Localization. - Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng:
Fine-Grained Dynamic Head for Object Detection. - Yuwen Xiong, Mengye Ren, Raquel Urtasun:
LoCo: Local Contrastive Representation Learning. - Katelyn Gao, Ozan Sener:
Modeling and Optimization Trade-off in Meta-learning. - Thomas P. Parnell, Andreea Anghel, Malgorzata Lazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis:
SnapBoost: A Heterogeneous Boosting Machine. - Yeshwanth Cherapanamjeri, Jelani Nelson:
On Adaptive Distance Estimation. - Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh:
Stage-wise Conservative Linear Bandits. - Sébastien Ehrhardt, Oliver Groth, Áron Monszpart, Martin Engelcke, Ingmar Posner, Niloy J. Mitra, Andrea Vedaldi:
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces. - Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu:
Metric-Free Individual Fairness in Online Learning. - Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen:
GreedyFool: Distortion-Aware Sparse Adversarial Attack. - Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. - Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, Junzhou Huang:
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist. - Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. - Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric:
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. - Han Cai, Chuang Gan, Ligeng Zhu, Song Han:
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning. - Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu:
RD$^2$: Reward Decomposition with Representation Decomposition. - Yixiao Ge, Feng Zhu, Dapeng Chen, Rui Zhao, Hongsheng Li:
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID. - Kevin Bello, Jean Honorio:
Fairness constraints can help exact inference in structured prediction. - Martín Bertrán, Natalia Martínez, Mariano Phielipp, Guillermo Sapiro:
Instance-based Generalization in Reinforcement Learning. - Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim:
Smooth And Consistent Probabilistic Regression Trees. - Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi:
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming. - R. James Cotton, Fabian H. Sinz, Andreas S. Tolias:
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses. - Pedro Savarese, Hugo Silva, Michael Maire:
Winning the Lottery with Continuous Sparsification. - Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan:
Adversarial robustness via robust low rank representations. - Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos:
Joints in Random Forests. - Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang:
Compositional Generalization by Learning Analytical Expressions. - Samuel S. Schoenholz, Ekin Dogus Cubuk:
JAX MD: A Framework for Differentiable Physics. - Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White:
An implicit function learning approach for parametric modal regression. - Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey:
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images. - Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec:
Coresets for Robust Training of Deep Neural Networks against Noisy Labels. - Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert:
Adapting to Misspecification in Contextual Bandits. - Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. - Jeongun Ryu, Jaewoong Shin, Haebeom Lee, Sung Ju Hwang:
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures. - Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau:
Learning to solve TV regularised problems with unrolled algorithms. - Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf:
Object-Centric Learning with Slot Attention. - Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge:
Improving robustness against common corruptions by covariate shift adaptation. - Damien Ackerer, Natasa Tagasovska, Thibault Vatter:
Deep Smoothing of the Implied Volatility Surface. - Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. - Sirisha Rambhatla, Xingguo Li, Jarvis D. Haupt:
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning. - Gunshi Gupta, Karmesh Yadav, Liam Paull:
Look-ahead Meta Learning for Continual Learning. - Ming Gao, Yi Ding, Bryon Aragam:
A polynomial-time algorithm for learning nonparametric causal graphs. - Jason M. Klusowski:
Sparse Learning with CART. - Mao Li, Yingyi Ma, Xinhua Zhang:
Proximal Mapping for Deep Regularization. - Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. - Shaobo Min, Hongtao Xie, Hantao Yao, Xuran Deng, Zheng-Jun Zha, Yongdong Zhang:
Hierarchical Granularity Transfer Learning. - Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl J. Friston:
Deep active inference agents using Monte-Carlo methods. - Alexander Ritchie, Robert A. Vandermeulen, Clayton D. Scott:
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. - Patrick Rubin-Delanchy:
Manifold structure in graph embeddings. - Zhenwei Dai, Anshumali Shrivastava:
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web. - Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han:
MCUNet: Tiny Deep Learning on IoT Devices. - Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In search of robust measures of generalization. - Xuezhou Zhang, Yuzhe Ma, Adish Singla:
Task-agnostic Exploration in Reinforcement Learning. - Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen:
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery. - Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. - Ling Pan, Qingpeng Cai, Longbo Huang:
Softmax Deep Double Deterministic Policy Gradients. - Ke Song, Wei Zhang, Ran Song, Yibin Li:
Online Decision Based Visual Tracking via Reinforcement Learning. - Gonçalo M. Correia, Vlad Niculae, Wilker Aziz, André F. T. Martins:
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. - Yaxing Wang, Lu Yu, Joost van de Weijer:
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs. - Hisham Husain:
Distributional Robustness with IPMs and links to Regularization and GANs. - François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer:
A shooting formulation of deep learning. - Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin:
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances. - Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung:
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning. - Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet:
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. - Siwei Wang, Longbo Huang, John C. S. Lui:
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits. - Kuang-Huei Lee, Ian Fischer, Anthony Z. Liu, Yijie Guo, Honglak Lee, John F. Canny, Sergio Guadarrama:
Predictive Information Accelerates Learning in RL. - Samuel B. Hopkins, Jerry Li, Fred Zhang:
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. - Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson:
High-Fidelity Generative Image Compression. - Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer:
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. - Aishwarya Sivaraman, Golnoosh Farnadi, Todd D. Millstein, Guy Van den Broeck:
Counterexample-Guided Learning of Monotonic Neural Networks. - Sara Rouhani, Tahrima Rahman, Vibhav Gogate:
A Novel Approach for Constrained Optimization in Graphical Models. - Quynh Nguyen, Marco Mondelli:
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology. - Cheng-Hsin Weng, Yan-Ting Lee, Shan-Hung Wu:
On the Trade-off between Adversarial and Backdoor Robustness. - Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui:
Implicit Graph Neural Networks. - Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Rethinking Importance Weighting for Deep Learning under Distribution Shift. - Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin:
Guiding Deep Molecular Optimization with Genetic Exploration. - Wenrui Zhang, Peng Li:
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks. - Dongxu Li, Chenchen Xu, Xin Yu, Kaihao Zhang, Benjamin Swift, Hanna Suominen, Hongdong Li:
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation. - Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J. Benjamin Hutchinson, Ajay B. Satpute, Jennifer G. Dy, Jan-Willem van de Meent:
Neural Topographic Factor Analysis for fMRI Data. - Robin Ru, Pedro M. Esperança, Fabio Maria Carlucci:
Neural Architecture Generator Optimization. - Kim Thang Nguyen:
A Bandit Learning Algorithm and Applications to Auction Design. - W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. - Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang:
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. - Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila:
Training Generative Adversarial Networks with Limited Data. - Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola B. Schönlieb:
Deeply Learned Spectral Total Variation Decomposition. - Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin:
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training. - Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi:
Improving Neural Network Training in Low Dimensional Random Bases. - Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal:
Safe Reinforcement Learning via Curriculum Induction. - Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal:
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? - Kaivalya Rawal, Himabindu Lakkaraju:
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. - Benjamin Aubin, Florent Krzakala, Yue M. Lu, Lenka Zdeborová:
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. - Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. - Minh N. Vu, My T. Thai:
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks. - Dingwen Zhang, Haibin Tian, Jungong Han:
Few-Cost Salient Object Detection with Adversarial-Paced Learning. - Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis:
Minimax Estimation of Conditional Moment Models. - Junzhe Zhang, Daniel Kumor, Elias Bareinboim:
Causal Imitation Learning With Unobserved Confounders. - Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio:
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling. - Jiancheng Yang, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao:
Learning Black-Box Attackers with Transferable Priors and Query Feedback. - Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang:
Locally Differentially Private (Contextual) Bandits Learning. - Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham:
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. - Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing Xu, Yunhe Wang:
Kernel Based Progressive Distillation for Adder Neural Networks. - Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai:
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization. - Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang:
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space. - Adil Salim, Anna Korba, Giulia Luise:
The Wasserstein Proximal Gradient Algorithm. - Eirikur Agustsson, Lucas Theis:
Universally Quantized Neural Compression. - Nicolò Campolongo, Francesco Orabona:
Temporal Variability in Implicit Online Learning. - Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart M. Shieber:
Investigating Gender Bias in Language Models Using Causal Mediation Analysis. - Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou:
Off-Policy Imitation Learning from Observations. - Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra:
Escaping Saddle-Point Faster under Interpolation-like Conditions. - Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matérn Gaussian Processes on Riemannian Manifolds. - Yang Song, Stefano Ermon:
Improved Techniques for Training Score-Based Generative Models. - Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael Auli:
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. - Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans:
A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs. - William S. Moses, Valentin Churavy:
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients. - Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang:
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? - Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess:
Value-driven Hindsight Modelling. - Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou:
Dynamic Regret of Convex and Smooth Functions. - Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang:
On Convergence of Nearest Neighbor Classifiers over Feature Transformations. - Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang:
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments. - Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu:
Contrastive learning of global and local features for medical image segmentation with limited annotations. - Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang:
Self-Supervised Graph Transformer on Large-Scale Molecular Data. - Jindong Jiang, Sungjin Ahn:
Generative Neurosymbolic Machines. - Jialun Zhang, Richard Y. Zhang:
How many samples is a good initial point worth in Low-rank Matrix Recovery? - Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin:
CSER: Communication-efficient SGD with Error Reset. - Edoardo Balzani, Kaushik J. Lakshminarasimhan, Dora E. Angelaki, Cristina Savin:
Efficient estimation of neural tuning during naturalistic behavior. - Andreas Gerhardus, Jakob Runge:
High-recall causal discovery for autocorrelated time series with latent confounders. - Juan Luis Gonzalez Bello, Munchurl Kim:
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes. - Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei:
Joint Contrastive Learning with Infinite Possibilities. - Jerry Li, Guanghao Ye:
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. - Adarsh K. Jeewajee, Leslie Pack Kaelbling:
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models. - Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz:
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. - Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. - Yonghan Jung, Jin Tian, Elias Bareinboim:
Learning Causal Effects via Weighted Empirical Risk Minimization. - Quang Minh Hoang, Nghia Hoang, Hai Pham, David P. Woodruff:
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. - Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez:
Incorporating Interpretable Output Constraints in Bayesian Neural Networks. - Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. - Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson:
Probabilistic Fair Clustering. - Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker:
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. - Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen:
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. - Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka:
Testing Determinantal Point Processes. - Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang:
CogLTX: Applying BERT to Long Texts. - Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang:
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning. - Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi:
Non-parametric Models for Non-negative Functions. - Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho:
Uncertainty Aware Semi-Supervised Learning on Graph Data. - Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan:
ConvBERT: Improving BERT with Span-based Dynamic Convolution. - Qizhang Li, Yiwen Guo, Hao Chen:
Practical No-box Adversarial Attacks against DNNs. - Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen:
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. - Hassan Mortagy, Swati Gupta, Sebastian Pokutta:
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization. - Alexander Shekhovtsov, Viktor Yanush, Boris Flach:
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. - Martin Klissarov, Doina Precup:
Reward Propagation Using Graph Convolutional Networks. - Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll:
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration. - Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam:
Fully Dynamic Algorithm for Constrained Submodular Optimization. - Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. - Clara Fannjiang, Jennifer Listgarten:
Autofocused oracles for model-based design. - Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse:
Debiasing Averaged Stochastic Gradient Descent to handle missing values. - Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel:
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. - Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash:
CompRess: Self-Supervised Learning by Compressing Representations. - Andrew D. McRae, Justin Romberg, Mark A. Davenport:
Sample complexity and effective dimension for regression on manifolds. - Dmitry Yarotsky, Anton Zhevnerchuk:
The phase diagram of approximation rates for deep neural networks. - Lifeng Shen, Zhuocong Li, James T. Kwok:
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. - Sachin Chauhan, Kashish Bansal, Rijurekha Sen:
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints. - Tao Fang, Yu Qi, Gang Pan:
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN. - Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine:
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. - Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner:
A Spectral Energy Distance for Parallel Speech Synthesis. - Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David D. Cox, James J. DiCarlo:
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. - Zayd Hammoudeh, Daniel Lowd:
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift. - Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi:
Deep Energy-based Modeling of Discrete-Time Physics. - Iro Laina, Ruth Fong, Andrea Vedaldi:
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning. - Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges:
Self-Learning Transformations for Improving Gaze and Head Redirection. - Simon Stepputtis, Joseph Campbell, Mariano J. Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor:
Language-Conditioned Imitation Learning for Robot Manipulation Tasks. - Bastian Alt, Matthias Schultheis, Heinz Koeppl:
POMDPs in Continuous Time and Discrete Spaces. - Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz:
Exemplar Guided Active Learning. - Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, Kui Jia:
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps. - Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
Node Embeddings and Exact Low-Rank Representations of Complex Networks. - Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. - Benoît Colange, Jaakko Peltonen, Michaël Aupetit, Denys Dutykh, Sylvain Lespinats:
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction. - Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang:
On Infinite-Width Hypernetworks. - Dmitry Igorevich Sorokin, Alexander E. Ulanov, Ekaterina A. Sazhina, Alexander I. Lvovsky:
Interferobot: aligning an optical interferometer by a reinforcement learning agent. - Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama:
Program Synthesis with Pragmatic Communication. - Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic:
Principal Neighbourhood Aggregation for Graph Nets. - Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. - Terrance DeVries, Michal Drozdzal, Graham W. Taylor:
Instance Selection for GANs. - Matthew Painter, Adam Prügel-Bennett, Jonathon S. Hare:
Linear Disentangled Representations and Unsupervised Action Estimation. - Youjian Zhang, Chaoyue Wang, Dacheng Tao:
Video Frame Interpolation without Temporal Priors. - Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue:
Learning compositional functions via multiplicative weight updates. - Eliran Shabat, Lee Cohen, Yishay Mansour:
Sample Complexity of Uniform Convergence for Multicalibration. - Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, Steven W. Su:
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement. - Friedrich Schüßler, Francesca Mastrogiuseppe, Alexis M. Dubreuil, Srdjan Ostojic, Omri Barak:
The interplay between randomness and structure during learning in RNNs. - Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. - Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter J. Castaldi, Jennifer G. Dy:
Instance-wise Feature Grouping. - Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik:
Robust Disentanglement of a Few Factors at a Time. - Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. - Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He:
Group Contextual Encoding for 3D Point Clouds. - Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier:
Latent Bandits Revisited. - Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj:
Is normalization indispensable for training deep neural network? - Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová:
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. - Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots:
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. - Meyer Scetbon, Marco Cuturi:
Linear Time Sinkhorn Divergences using Positive Features. - Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz:
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. - Ziqi Ke, Haris Vikalo:
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction. - Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang:
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method. - Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan:
Adversarial Counterfactual Learning and Evaluation for Recommender System. - Giorgos Mamakoukas, Orest Xherija, Todd D. Murphey:
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control. - Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le:
Evolving Normalization-Activation Layers. - Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan:
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. - Cheng Chi, Fangyun Wei, Han Hu:
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder. - Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov:
Efficient Learning of Discrete Graphical Models. - Ilias Diakonikolas, Daniel Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. - Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin C. Rinard, Armando Solar-Lezama:
Neurosymbolic Transformers for Multi-Agent Communication. - Marwa El Halabi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization: Algorithms and Hardness. - Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, Anupam Datta:
Smoothed Geometry for Robust Attribution. - Sascha Saralajew, Lars Holdijk, Thomas Villmann:
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. - Mikko Lauri, Frans A. Oliehoek:
Multi-agent active perception with prediction rewards. - Pablo Tano, Peter Dayan, Alexandre Pouget:
A Local Temporal Difference Code for Distributional Reinforcement Learning. - Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi:
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions. - Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas:
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations. - Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin:
Deep Automodulators. - Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning. - Zahra S. Razaee, Arash A. Amini:
The Potts-Ising model for discrete multivariate data. - Shailee Jain, Vy A. Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth:
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech. - Semih Cayci, Swati Gupta, Atilla Eryilmaz:
Group-Fair Online Allocation in Continuous Time. - Gang Wang, Songtao Lu, Georgios B. Giannakis, Gerald Tesauro, Jian Sun:
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. - Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong:
Understanding Gradient Clipping in Private SGD: A Geometric Perspective. - Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. - Stephen L. Keeley, Mikio C. Aoi, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow:
Identifying signal and noise structure in neural population activity with Gaussian process factor models. - Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh:
Equivariant Networks for Hierarchical Structures. - Luca De Gennaro Aquino, Stephan Eckstein:
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics. - Mehdi Rezaee, Francis Ferraro:
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick. - Yanqi Zhou, Sudip Roy, AmirAli Abdolrashidi, Daniel Wong, Peter C. Ma, Qiumin Xu, Hanxiao Liu, Mangpo Phitchaya Phothilimtha, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon:
Transferable Graph Optimizers for ML Compilers. - Akash Saha, Balamurugan Palaniappan:
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces. - Jaeho Lee, Sejun Park, Jinwoo Shin:
Learning Bounds for Risk-sensitive Learning. - Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. - Robert Geirhos, Kristof Meding, Felix A. Wichmann:
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency. - Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. - Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis:
Constant-Expansion Suffices for Compressed Sensing with Generative Priors. - Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin:
RANet: Region Attention Network for Semantic Segmentation. - Zhenyu Liao, Romain Couillet, Michael W. Mahoney:
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. - Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell:
Learning sparse codes from compressed representations with biologically plausible local wiring constraints. - Yunhao Tang:
Self-Imitation Learning via Generalized Lower Bound Q-learning. - Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. - Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng:
Directional Pruning of Deep Neural Networks. - Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab S. Mirrokni, Lijie Ren:
Smoothly Bounding User Contributions in Differential Privacy. - Minjia Zhang, Yuxiong He:
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. - Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor:
Online Planning with Lookahead Policies. - Ethan Weinberger, Joseph D. Janizek, Su-In Lee:
Learning Deep Attribution Priors Based On Prior Knowledge. - Eli Moore, Rishidev Chaudhuri:
Using noise to probe recurrent neural network structure and prune synapses. - Sang-gil Lee, Sungwon Kim, Sungroh Yoon:
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity. - Chaoyang He, Murali Annavaram, Salman Avestimehr:
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. - Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro:
Neural FFTs for Universal Texture Image Synthesis. - Maosen Li, Siheng Chen, Ya Zhang, Ivor W. Tsang:
Graph Cross Networks with Vertex Infomax Pooling. - Hilal Asi, John C. Duchi:
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. - Nelson Vadori, Sumitra Ganesh, Prashant P. Reddy, Manuela Veloso:
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games. - Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. - Clément Vignac, Andreas Loukas, Pascal Frossard:
Building powerful and equivariant graph neural networks with structural message-passing. - Sebastian Curi, Felix Berkenkamp, Andreas Krause:
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. - Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
Practical Low-Rank Communication Compression in Decentralized Deep Learning. - Kanishk Gandhi, Brenden M. Lake:
Mutual exclusivity as a challenge for deep neural networks. - Edward J. Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal:
3D Shape Reconstruction from Vision and Touch. - Taojiannan Yang, Sijie Zhu, Chen Chen:
GradAug: A New Regularization Method for Deep Neural Networks. - Scott Fujimoto, David Meger, Doina Precup:
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. - Hu Fu, Tao Lin:
Learning Utilities and Equilibria in Non-Truthful Auctions. - Nicolas Boullé, Yuji Nakatsukasa, Alex Townsend:
Rational neural networks. - Michal J. Tyszkiewicz, Pascal Fua, Eduard Trulls:
DISK: Learning local features with policy gradient. - Masaaki Takada, Hironori Fujisawa:
Transfer Learning via ℓ1 Regularization. - Prune Truong, Martin Danelljan, Luc Van Gool, Radu Timofte:
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network. - Gabriel Kalweit, Maria Hügle, Moritz Werling, Joschka Boedecker:
Deep Inverse Q-learning with Constraints. - Chaobing Song, Zhengyuan Zhou, Yichao Zhou, Yong Jiang, Yi Ma:
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities. - Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni:
Prediction with Corrupted Expert Advice. - Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao:
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency. - Zijun Cui, Tengfei Song, Yuru Wang, Qiang Ji:
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition. - Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman:
Point process models for sequence detection in high-dimensional neural spike trains. - Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta:
Adversarial Attacks on Linear Contextual Bandits. - K. J. Joseph, Vineeth Nallure Balasubramanian:
Meta-Consolidation for Continual Learning. - Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo:
Organizing recurrent network dynamics by task-computation to enable continual learning. - Jorge A. Mendez, Boyu Wang, Eric Eaton:
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting. - Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi:
Kernel Methods Through the Roof: Handling Billions of Points Efficiently. - Kolyan Ray, Botond Szabó, Gabriel Clara:
Spike and slab variational Bayes for high dimensional logistic regression. - Long Zhao, Ting Liu, Xi Peng, Dimitris N. Metaxas:
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness. - Jean Feydy, Joan Alexis Glaunès, Benjamin Charlier, Michael M. Bronstein:
Fast geometric learning with symbolic matrices. - Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang:
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler. - Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman:
CoinPress: Practical Private Mean and Covariance Estimation. - Ruosong Wang, Peilin Zhong, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang:
Planning with General Objective Functions: Going Beyond Total Rewards. - Yimeng Min, Frederik Wenkel, Guy Wolf:
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. - Elfarouk Harb, Ho Shan Lam:
KFC: A Scalable Approximation Algorithm for $k$-center Fair Clustering. - Yingying Li, Na Li:
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms. - Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora:
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. - Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen:
Scalable Graph Neural Networks via Bidirectional Propagation. - Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin:
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning. - Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan:
Assisted Learning: A Framework for Multi-Organization Learning. - Johan Larsson, Malgorzata Bogdan, Jonas Wallin:
The Strong Screening Rule for SLOPE. - Meiyi Ma, Ji Gao, Lu Feng, John A. Stankovic:
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks. - Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon:
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks. - Omar Montasser, Steve Hanneke, Nati Srebro:
Reducing Adversarially Robust Learning to Non-Robust PAC Learning. - Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena:
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples. - Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atilim Gunes Baydin:
Black-Box Optimization with Local Generative Surrogates. - Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini:
Efficient Generation of Structured Objects with Constrained Adversarial Networks. - Huan Fu, Shunming Li, Rongfei Jia, Mingming Gong, Binqiang Zhao, Dacheng Tao:
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning. - Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal:
Recovery of sparse linear classifiers from mixture of responses. - Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran:
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning. - Omid Sadeghi, Prasanna Sanjay Raut, Maryam Fazel:
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints. - Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig:
Learning Sparse Prototypes for Text Generation. - Li Jing, Jure Zbontar, Yann LeCun:
Implicit Rank-Minimizing Autoencoder. - Jianda Chen, Shangyu Chen, Sinno Jialin Pan:
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. - Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao:
Task-Oriented Feature Distillation. - Spencer Compton, Murat Kocaoglu, Kristjan H. Greenewald, Dmitriy Katz:
Entropic Causal Inference: Identifiability and Finite Sample Results. - Ben Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov:
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. - Shaocong Ma, Yi Zhou, Shaofeng Zou:
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. - Menghao Li, Minjia Zhang, Chi Wang, Mingqin Li:
AdaTune: Adaptive Tensor Program Compilation Made Efficient. - Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
When Do Neural Networks Outperform Kernel Methods? - Arnab Ghosh, Harkirat S. Behl, Emilien Dupont, Philip H. S. Torr, Vinay P. Namboodiri:
STEER : Simple Temporal Regularization For Neural ODE. - Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu:
A Variational Approach for Learning from Positive and Unlabeled Data. - Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li:
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut. - Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman:
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. - Zalán Borsos, Mojmir Mutny, Andreas Krause:
Coresets via Bilevel Optimization for Continual Learning and Streaming. - Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. - Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu:
Understanding and Exploring the Network with Stochastic Architectures. - Jean Barbier, Nicolas Macris, Cynthia Rush:
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation. - Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus:
Deep Evidential Regression. - Randall Balestriero, Sébastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. - Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell:
Bayesian Pseudocoresets. - Victoria Dean, Shubham Tulsiani, Abhinav Gupta:
See, Hear, Explore: Curiosity via Audio-Visual Association. - Kevin Roth, Yannic Kilcher, Thomas Hofmann:
Adversarial Training is a Form of Data-dependent Operator Norm Regularization. - David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri B. Chklovskii:
A Biologically Plausible Neural Network for Slow Feature Analysis. - Lai Tian, Feiping Nie, Rong Wang, Xuelong Li:
Learning Feature Sparse Principal Subspace. - Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz:
Online Adaptation for Consistent Mesh Reconstruction in the Wild. - Kush Bhatia, Karthik Sridharan:
Online learning with dynamics: A minimax perspective. - Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai:
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. - Eduard Gorbunov, Marina Danilova, Alexander V. Gasnikov:
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. - Peter W. Glynn, Ramesh Johari, Mohammad Rasouli:
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach. - Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré:
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. - A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli:
The Autoencoding Variational Autoencoder. - Jaewoong Cho, Gyeongjo Hwang, Changho Suh:
A Fair Classifier Using Kernel Density Estimation. - Francesco Cosentino, Harald Oberhauser, Alessandro Abate:
A Randomized Algorithm to Reduce the Support of Discrete Measures. - Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi:
Distributionally Robust Federated Averaging. - Cyrus Cousins, Matteo Riondato:
Sharp uniform convergence bounds through empirical centralization. - Gedas Bertasius, Lorenzo Torresani:
COBE: Contextualized Object Embeddings from Narrated Instructional Video. - Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye:
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control. - Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. - Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi:
Supermasks in Superposition. - Jorio Cocola, Paul Hand, Vladislav Voroninski:
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors. - Zihan Zhang, Yuan Zhou, Xiangyang Ji:
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition. - Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha:
Learning to Incentivize Other Learning Agents. - Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong Li:
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation. - Viet Anh Nguyen, Fan Zhang, José H. Blanchet, Erick Delage, Yinyu Ye:
Distributionally Robust Local Non-parametric Conditional Estimation. - Yunpeng Shi, Shaohan Li, Gilad Lerman:
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian. - Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver:
Meta-Gradient Reinforcement Learning with an Objective Discovered Online. - Yiling Chen, Yang Liu, Chara Podimata:
Learning Strategy-Aware Linear Classifiers. - Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang:
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss. - Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania:
Calibrating Deep Neural Networks using Focal Loss. - N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai:
Optimizing Mode Connectivity via Neuron Alignment. - Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun:
Information Theoretic Regret Bounds for Online Nonlinear Control. - Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton:
A kernel test for quasi-independence. - Yiming Zhang, Quan Vuong, Keith W. Ross:
First Order Constrained Optimization in Policy Space. - Étienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson:
Learning Augmented Energy Minimization via Speed Scaling. - Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil:
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. - Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. - Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? - Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang:
Neural Sparse Representation for Image Restoration. - Kaiwen Zhou, Anthony Man-Cho So, James Cheng:
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates. - Gamal Sallam, Zizhan Zheng, Jie Wu, Bo Ji:
Robust Sequence Submodular Maximization. - Xingchao Liu, Xing Han, Na Zhang, Qiang Liu:
Certified Monotonic Neural Networks. - Cornelius Schröder, David A. Klindt, Sarah Strauß, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens:
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina. - Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino:
Efficient Algorithms for Device Placement of DNN Graph Operators. - Juan Gamella, Christina Heinze-Deml:
Active Invariant Causal Prediction: Experiment Selection through Stability. - Henry B. Moss, David S. Leslie, Daniel Beck, Javier Gonzalez, Paul Rayson:
BOSS: Bayesian Optimization over String Spaces. - Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux:
Model Interpretability through the lens of Computational Complexity. - Christian A. Naesseth, Fredrik Lindsten, David M. Blei:
Markovian Score Climbing: Variational Inference with KL(p||q). - Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang:
Improved Analysis of Clipping Algorithms for Non-convex Optimization. - Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang:
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs. - Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan:
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection. - Guangmo Tong:
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks. - Donghwan Lee, Niao He:
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. - Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel:
Kernel Alignment Risk Estimator: Risk Prediction from Training Data. - Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai:
Calibrating CNNs for Lifelong Learning. - Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi:
Online Convex Optimization Over Erdos-Renyi Random Networks. - Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patané, Luca Bortolussi, Guido Sanguinetti:
Robustness of Bayesian Neural Networks to Gradient-Based Attacks. - Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu:
Parametric Instance Classification for Unsupervised Visual Feature learning. - Md Aamir Raihan, Tor M. Aamodt:
Sparse Weight Activation Training. - Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe:
Collapsing Bandits and Their Application to Public Health Intervention. - Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt:
Neural Sparse Voxel Fields. - Bruno Lecouat, Jean Ponce, Julien Mairal:
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding. - Clément L. Canonne, Gautam Kamath, Thomas Steinke:
The Discrete Gaussian for Differential Privacy. - Arun Jambulapati, Jerry Li, Kevin Tian:
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. - Ayoub El Hanchi, David A. Stephens:
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes. - Colin Bredenberg, Eero P. Simoncelli, Cristina Savin:
Learning efficient task-dependent representations with synaptic plasticity. - Wei Deng, Guang Lin, Faming Liang:
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. - Tian Xu, Ziniu Li, Yang Yu:
Error Bounds of Imitating Policies and Environments. - Le Zhang, Ryutaro Tanno, Moucheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel C. Alexander:
Disentangling Human Error from Ground Truth in Segmentation of Medical Images. - Simon Zhuang, Dylan Hadfield-Menell:
Consequences of Misaligned AI. - Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Chris Pal:
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. - Bowen Baker:
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences. - Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein:
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics. - Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su:
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs. - Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey:
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. - Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin:
The Lottery Ticket Hypothesis for Pre-trained BERT Networks. - Shuxiao Chen, Hangfeng He, Weijie J. Su:
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity. - Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser:
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. - Hadi Mohaghegh Dolatabadi, Sarah M. Erfani, Christopher Leckie:
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows. - Yijun Li, Richard Zhang, Jingwan Lu, Eli Shechtman:
Few-shot Image Generation with Elastic Weight Consolidation. - Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner:
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. - Fan Zhou, Jianing Wang, Xingdong Feng:
Non-Crossing Quantile Regression for Distributional Reinforcement Learning. - Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara:
Dark Experience for General Continual Learning: a Strong, Simple Baseline. - Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, Changjie Fan:
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping. - Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu:
Neural encoding with visual attention. - Chaoyue Liu, Libin Zhu, Mikhail Belkin:
On the linearity of large non-linear models: when and why the tangent kernel is constant. - Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman:
PLLay: Efficient Topological Layer based on Persistent Landscapes. - Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram:
Decentralized Langevin Dynamics for Bayesian Learning. - Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner:
Shared Space Transfer Learning for analyzing multi-site fMRI data. - Shaofeng Zhang, Meng Liu, Junchi Yan:
The Diversified Ensemble Neural Network. - Santosh K. Srivastava, Dinesh Khandelwal, Dhiraj Madan, Dinesh Garg, Hima Karanam, L. Venkata Subramaniam:
Inductive Quantum Embedding. - Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. - Nikolai Karpov, Qin Zhang:
Batched Coarse Ranking in Multi-Armed Bandits. - Maksym Andriushchenko, Nicolas Flammarion:
Understanding and Improving Fast Adversarial Training. - M. Nikhil Krishnan, Seyederfan Hosseini, Ashish Khisti:
Coded Sequential Matrix Multiplication For Straggler Mitigation. - Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. - Julian Bitterwolf, Alexander Meinke, Matthias Hein:
Certifiably Adversarially Robust Detection of Out-of-Distribution Data. - Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao:
Domain Generalization via Entropy Regularization. - Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey:
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels. - Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian J. Zhang:
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment. - Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato:
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. - Konstantin Makarychev, Aravind Reddy, Liren Shan:
Improved Guarantees for k-means++ and k-means++ Parallel. - Anthony Tompkins, Rafael Oliveira, Fabio T. Ramos:
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning. - Chong Zhang, Huan Zhang, Cho-Jui Hsieh:
An Efficient Adversarial Attack for Tree Ensembles. - Zijie Huang, Yizhou Sun, Wei Wang:
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations. - Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti:
Online Bayesian Persuasion. - Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang:
Robust Pre-Training by Adversarial Contrastive Learning. - Giannis Nikolentzos, Michalis Vazirgiannis:
Random Walk Graph Neural Networks. - Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos:
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling. - Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson:
Fast and Accurate $k$-means++ via Rejection Sampling. - Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler:
Variational Amodal Object Completion. - Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang:
When Counterpoint Meets Chinese Folk Melodies. - Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh:
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces. - Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko:
Universal Domain Adaptation through Self Supervision. - Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis:
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning. - Zhi Kou, Kaichao You, Mingsheng Long, Jianmin Wang:
Stochastic Normalization. - Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun:
Constrained episodic reinforcement learning in concave-convex and knapsack settings. - Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar:
On Learning Ising Models under Huber's Contamination Model. - Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey:
Cross-validation Confidence Intervals for Test Error. - Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte:
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation. - Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou:
Bayesian Attention Modules. - Kevin Scaman, Cédric Malherbe:
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. - Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim:
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds. - Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels:
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. - Mao Ye, Lemeng Wu, Qiang Liu:
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. - Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò:
Path Integral Based Convolution and Pooling for Graph Neural Networks. - Ioana Bica, James Jordon, Mihaela van der Schaar:
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. - Heejong Bong, Zongge Liu, Zhao Ren, Matthew A. Smith, Valérie Ventura, Robert E. Kass:
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. - Hassan Hafez-Kolahi, Zeinab Golgooni, Shohreh Kasaei, Mahdieh Soleymani:
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds. - Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar:
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. - Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin:
GAN Memory with No Forgetting. - Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang:
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games. - David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness:
Gaussian Gated Linear Networks. - Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan:
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding. - Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin:
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. - Nikita Doikov, Yurii E. Nesterov:
Convex optimization based on global lower second-order models. - Tiancheng Jin, Haipeng Luo:
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition. - Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. - Xiao Zhang, Michael Maire:
Self-Supervised Visual Representation Learning from Hierarchical Grouping. - Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther:
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds. - Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes:
Explicit Regularisation in Gaussian Noise Injections. - Julius Berner, Markus Dablander, Philipp Grohs:
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. - Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang:
Finite-Time Analysis for Double Q-learning. - Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, Amos Sironi:
Learning to Detect Objects with a 1 Megapixel Event Camera. - Jiayang Li, Jing Yu, Yu Marco Nie, Zhaoran Wang:
End-to-End Learning and Intervention in Games. - Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. - Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli:
Predictive coding in balanced neural networks with noise, chaos and delays. - Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan V. Oseledets:
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. - Young Hun Jung, Baekjin Kim, Ambuj Tewari:
On the Equivalence between Online and Private Learnability beyond Binary Classification. - A. J. Piergiovanni, Michael S. Ryoo:
AViD Dataset: Anonymized Videos from Diverse Countries. - Luiz F. O. Chamon, Alejandro Ribeiro:
Probably Approximately Correct Constrained Learning. - Riccardo Del Chiaro, Bartlomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer:
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning. - Stratis Tsirtsis, Manuel Gomez Rodriguez:
Decisions, Counterfactual Explanations and Strategic Behavior. - Shir Gur, Sagie Benaim, Lior Wolf:
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample. - Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang:
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization. - Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan, Florent Krzakala:
Reservoir Computing meets Recurrent Kernels and Structured Transforms. - Zeyi Huang, Yang Zou, B. V. K. Vijaya Kumar, Dong Huang:
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection. - Shiva Kaul:
Linear Dynamical Systems as a Core Computational Primitive. - Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li:
Ratio Trace Formulation of Wasserstein Discriminant Analysis. - Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor:
PAC-Bayes Analysis Beyond the Usual Bounds. - Youngsung Kim, Jinwoo Shin, Eunho Yang, Sung Ju Hwang:
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning. - Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu:
MPNet: Masked and Permuted Pre-training for Language Understanding. - Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. - Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Busic, Sean P. Meyn:
Zap Q-Learning With Nonlinear Function Approximation. - Sungyoon Lee, Jaewook Lee, Saerom Park:
Lipschitz-Certifiable Training with a Tight Outer Bound. - Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser:
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint. - Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal:
Conformal Symplectic and Relativistic Optimization. - Mingyuan Zhang, Shivani Agarwal:
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class. - Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller:
Inverting Gradients - How easy is it to break privacy in federated learning? - Nisheet Patel, Luigi Acerbi, Alexandre Pouget:
Dynamic allocation of limited memory resources in reinforcement learning. - Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg:
CryptoNAS: Private Inference on a ReLU Budget. - Gersende Fort, Eric Moulines, Hoi-To Wai:
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm. - Makan Arastuie, Subhadeep Paul, Kevin S. Xu:
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation. - Xiaoya Li, Yuxian Meng, Mingxin Zhou, Qinghong Han, Fei Wu, Jiwei Li:
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection. - Jiaxuan You, Zhitao Ying, Jure Leskovec:
Design Space for Graph Neural Networks. - Jungil Kong, Jaehyeon Kim, Jaekyoung Bae:
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis. - Youssef Mroueh, Mattia Rigotti:
Unbalanced Sobolev Descent. - Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger:
Identifying Mislabeled Data using the Area Under the Margin Ranking. - Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong:
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games. - Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing:
High-Throughput Synchronous Deep RL. - Chih-Hui Ho, Nuno Vasconcelos:
Contrastive Learning with Adversarial Examples. - Guangyao Zhou:
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables. - Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang:
Adversarial Sparse Transformer for Time Series Forecasting. - Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington:
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. - Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng:
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration. - Theofanis Karaletsos, Thang D. Bui:
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. - Jesse Mu, Jacob Andreas:
Compositional Explanations of Neurons. - Peng Cui, Wenbo Hu, Jun Zhu:
Calibrated Reliable Regression using Maximum Mean Discrepancy. - Ziwei Ji, Matus Telgarsky:
Directional convergence and alignment in deep learning. - Siddhant Garg, Yingyu Liang:
Functional Regularization for Representation Learning: A Unified Theoretical Perspective. - Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts:
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits. - Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions With Additive Importance Measures. - Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier:
Online Non-Convex Optimization with Imperfect Feedback. - Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang:
Co-Tuning for Transfer Learning. - Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu:
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. - Natalia Neverova, David Novotný, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi:
Continuous Surface Embeddings. - Sai Qian Zhang, Qi Zhang, Jieyu Lin:
Succinct and Robust Multi-Agent Communication With Temporal Message Control. - Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed:
Big Bird: Transformers for Longer Sequences. - Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. - Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik:
Random Reshuffling: Simple Analysis with Vast Improvements. - Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine:
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Jagarlapudi Saketha Nath, Pratik Kumar Jawanpuria:
Statistical Optimal Transport posed as Learning Kernel Embedding. - Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu:
Dual-Resolution Correspondence Networks. - Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. - Neng Wan, Dapeng Li, Naira Hovakimyan:
f-Divergence Variational Inference. - Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias:
Unfolding recurrence by Green's functions for optimized reservoir computing. - Yihao Lv, Youzhi Gu, Xinggao Liu:
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification. - David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière:
Disentangling by Subspace Diffusion. - Zachary Brown, Nathaniel R. Robinson, David Wingate, Nancy Fulda:
Towards Neural Programming Interfaces. - Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho:
Discovering Symbolic Models from Deep Learning with Inductive Biases. - Wojciech M. Czarnecki, Gauthier Gidel, Brendan D. Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi, Max Jaderberg:
Real World Games Look Like Spinning Tops. - Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang:
Cooperative Heterogeneous Deep Reinforcement Learning. - Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun:
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization. - Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier:
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. - Xiangru Huang, Haitao Yang, Etienne Vouga, Qixing Huang:
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs. - Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot:
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. - Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath:
Applications of Common Entropy for Causal Inference. - Kwangjun Ahn, Chulhee Yun, Suvrit Sra:
SGD with shuffling: optimal rates without component convexity and large epoch requirements. - Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro:
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models. - Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Neural Manifold Ordinary Differential Equations. - Titouan Vayer, Ievgen Redko, Rémi Flamary, Nicolas Courty:
CO-Optimal Transport. - James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone:
Continuous Meta-Learning without Tasks. - Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto:
A mathematical theory of cooperative communication. - Avetik G. Karagulyan, Arnak S. Dalalyan:
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets. - Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Learning Invariances in Neural Networks from Training Data. - Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. - Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun:
Pruning Filter in Filter. - Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu:
Learning to Mutate with Hypergradient Guided Population. - Yunzhang Zhu:
A convex optimization formulation for multivariate regression. - Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, Timothy M. Hospedales:
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods. - Jonathan Niles-Weed, Ilias Zadik:
The All-or-Nothing Phenomenon in Sparse Tensor PCA. - Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song:
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis. - Jiahao Su, Shiqi Wang, Furong Huang:
ARMA Nets: Expanding Receptive Field for Dense Prediction. - Mina Konakovic-Lukovic, Yunsheng Tian, Wojciech Matusik:
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations. - Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen:
SOLOv2: Dynamic and Fast Instance Segmentation. - Chong You, Zhihui Zhu, Qing Qu, Yi Ma:
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization. - Ritesh Noothigattu, Dominik Peters, Ariel D. Procaccia:
Axioms for Learning from Pairwise Comparisons. - Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon:
Continuous Regularized Wasserstein Barycenters. - Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang:
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. - Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Multitask Learning with Long-Term Memory. - Yuehua Zhu, Muli Yang, Cheng Deng, Wei Liu:
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies. - Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang:
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. - Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov:
On Reward-Free Reinforcement Learning with Linear Function Approximation. - Sandrine Péché, Vianney Perchet:
Robustness of Community Detection to Random Geometric Perturbations. - Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar:
Learning outside the Black-Box: The pursuit of interpretable models. - Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization. - Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust large-margin learning in hyperbolic space. - Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang:
Replica-Exchange Nosé-Hoover Dynamics for Bayesian Learning on Large Datasets. - Micah Goldblum, Liam Fowl, Tom Goldstein:
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. - Guillermo Ortiz-Jiménez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard:
Neural Anisotropy Directions. - Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, Andrew Lim:
Digraph Inception Convolutional Networks. - Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson:
PAC-Bayesian Bound for the Conditional Value at Risk. - Jackson Gorham, Anant Raj, Lester Mackey:
Stochastic Stein Discrepancies. - Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. - Houwen Peng, Hao Du, Hongyuan Yu, Qi Li, Jing Liao, Jianlong Fu:
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search. - Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu:
Fair Multiple Decision Making Through Soft Interventions. - Yuan Chen, Donglin Zeng, Tianchen Xu, Yuanjia Wang:
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment. - Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. - Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth:
Inverse Learning of Symmetries. - Moshe Eliasof, Eran Treister:
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling. - Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar:
Distributed Newton Can Communicate Less and Resist Byzantine Workers. - Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett:
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. - Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts:
Effective Diversity in Population Based Reinforcement Learning. - Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee:
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data. - Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow:
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. - Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio:
Hybrid Models for Learning to Branch. - Sidak Pal Singh, Dan Alistarh:
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression. - Fan Bao, Chongxuan Li, Taufik Xu, Hang Su, Jun Zhu, Bo Zhang:
Bi-level Score Matching for Learning Energy-based Latent Variable Models. - Zhu Zhang, Zhou Zhao, Zhijie Lin, Jieming Zhu, Xiuqiang He:
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding. - Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand:
Decision trees as partitioning machines to characterize their generalization properties. - Mingzhe Wang, Jia Deng:
Learning to Prove Theorems by Learning to Generate Theorems. - Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gärtner, Benjamin Bergner, Christoph Lippert:
3D Self-Supervised Methods for Medical Imaging. - Laurence Aitchison:
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods. - Justin Y. Chen, Gregory Valiant, Paul Valiant:
Worst-Case Analysis for Randomly Collected Data. - Yiling Chen, Yiheng Shen, Shuran Zheng:
Truthful Data Acquisition via Peer Prediction. - Muhammad Osama, Dave Zachariah, Peter Stoica:
Learning Robust Decision Policies from Observational Data. - Jiani Li, Waseem Abbas, Xenofon D. Koutsoukos:
Byzantine Resilient Distributed Multi-Task Learning. - Ziping Xu, Ambuj Tewari:
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting. - Ranganath Krishnan, Omesh Tickoo:
Improving model calibration with accuracy versus uncertainty optimization. - Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling:
The Convolution Exponential and Generalized Sylvester Flows. - Yanli Liu, Yuan Gao, Wotao Yin:
An Improved Analysis of Stochastic Gradient Descent with Momentum. - Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney:
Precise expressions for random projections: Low-rank approximation and randomized Newton. - Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell:
The MAGICAL Benchmark for Robust Imitation. - Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath:
X-CAL: Explicit Calibration for Survival Analysis. - Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang:
Decentralized Accelerated Proximal Gradient Descent. - Max Simchowitz:
Making Non-Stochastic Control (Almost) as Easy as Stochastic. - Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian J. McAuley, Ke Xu, Furu Wei:
BERT Loses Patience: Fast and Robust Inference with Early Exit. - Dmitry Kovalev, Adil Salim, Peter Richtárik:
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. - Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith W. Ross:
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning. - Edo Cohen-Karlik, Avichai Ben David, Amir Globerson:
Regularizing Towards Permutation Invariance In Recurrent Models. - Herman Yau, Chris Russell, Simon Hadfield:
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes. - Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. - Arpit Agarwal, Nicholas Johnson, Shivani Agarwal:
Choice Bandits. - Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin:
What if Neural Networks had SVDs? - Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang:
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices. - Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje:
CoMIR: Contrastive Multimodal Image Representation for Registration. - Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel J. Hsu:
Ensuring Fairness Beyond the Training Data. - Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How do fair decisions fare in long-term qualification? - Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer:
Pre-training via Paraphrasing. - Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut T. Kandemir:
GCN meets GPU: Decoupling "When to Sample" from "How to Sample". - Zixuan Ke, Bing Liu, Xingchang Huang:
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks. - Christian J. Walder, Richard Nock:
All your loss are belong to Bayes. - Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer:
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. - Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu:
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. - Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. - Shinji Ito:
A Tight Lower Bound and Efficient Reduction for Swap Regret. - Aviral Kumar, Abhishek Gupta, Sergey Levine:
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. - Viet Huynh, He Zhao, Dinh Phung:
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. - Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt:
Measuring Robustness to Natural Distribution Shifts in Image Classification. - Disi Ji, Padhraic Smyth, Mark Steyvers:
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. - Ekin Dogus Cubuk, Barret Zoph, Jonathon Shlens, Quoc Le:
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space. - Yiwei Shen, Pierre C. Bellec:
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model. - Zhe Dong, Andriy Mnih, George Tucker:
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. - Pantelis Elinas, Edwin V. Bonilla, Louis C. Tiao:
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. - Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan:
Supervised Contrastive Learning. - Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam:
Learning Optimal Representations with the Decodable Information Bottleneck. - Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega:
Meta-trained agents implement Bayes-optimal agents. - Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan:
Learning Agent Representations for Ice Hockey. - Kevin Course, Trefor W. Evans, Prasanth B. Nair:
Weak Form Generalized Hamiltonian Learning. - Aljaz Bozic, Pablo R. Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Nießner:
Neural Non-Rigid Tracking. - Etai Littwin, Ben Myara, Sima Sabah, Joshua M. Susskind, Shuangfei Zhai, Oren Golan:
Collegial Ensembles. - Wenda Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo:
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection. - Cheng Zhang:
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows. - Dingyi Zhang, Yingming Li, Zhongfei Zhang:
Deep Metric Learning with Spherical Embedding. - Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski:
Preference-based Reinforcement Learning with Finite-Time Guarantees. - Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha C. Dvornek, Xenophon Papademetris, James S. Duncan:
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients. - Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for Covid-19 Forecasting. - Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill:
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. - Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer:
Modern Hopfield Networks and Attention for Immune Repertoire Classification. - Heng Yang, Luca Carlone:
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers. - Liam Collins, Aryan Mokhtari, Sanjay Shakkottai:
Task-Robust Model-Agnostic Meta-Learning. - Sergey Shuvaev, Sarah Starosta, Duda Kvitsiani, Ádám Kepecs, Alexei A. Koulakov:
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making. - Dan Garber:
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. - Xiao Wang, Qi Lei, Ioannis Panageas:
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev. - Allen Liu, Ankur Moitra:
Tensor Completion Made Practical. - Kenta Oono, Taiji Suzuki:
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks. - Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz:
Content Provider Dynamics and Coordination in Recommendation Ecosystems. - Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni:
Almost Surely Stable Deep Dynamics. - Tim Bakker, Herke van Hoof, Max Welling:
Experimental design for MRI by greedy policy search. - Aaron Sonabend W., Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits:
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation. - Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano:
ColdGANs: Taming Language GANs with Cautious Sampling Strategies. - Xi Chen, Binghui Peng:
Hedging in games: Faster convergence of external and swap regrets. - Katherine L. Hermann, Ting Chen, Simon Kornblith:
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks. - In Huh, Eunho Yang, Sung Ju Hwang, Jinwoo Shin:
Time-Reversal Symmetric ODE Network. - Jimit Majmudar, Stephen A. Vavasis:
Provable Overlapping Community Detection in Weighted Graphs. - Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Fast Unbalanced Optimal Transport on a Tree. - Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. - Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg:
Avoiding Side Effects By Considering Future Tasks. - Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel J. Kochenderfer, Jure Leskovec:
Handling Missing Data with Graph Representation Learning. - Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, Wanli Ouyang:
Improving Auto-Augment via Augmentation-Wise Weight Sharing. - Zhennan Wang, Canqun Xiang, Wenbin Zou, Chen Xu:
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles. - Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu:
HRN: A Holistic Approach to One Class Learning. - Zhaoqiang Liu, Jonathan Scarlett:
The Generalized Lasso with Nonlinear Observations and Generative Priors. - Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression via plug-in estimator and recalibration with statistical guarantees. - Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. - Roshan Shariff, Csaba Szepesvári:
Efficient Planning in Large MDPs with Weak Linear Function Approximation. - Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. - Tong Chen, Jean B. Lasserre, Victor Magron, Edouard Pauwels:
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks. - Ayush Jain, Alon Orlitsky:
Linear-Sample Learning of Low-Rank Distributions. - Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. - Taiji Suzuki:
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics. - Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka:
Online Bayesian Goal Inference for Boundedly Rational Planning Agents. - Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Xiaoning Qian:
BayReL: Bayesian Relational Learning for Multi-omics Data Integration. - Abhishek Sharma, Maks Ovsjanikov:
Weakly Supervised Deep Functional Maps for Shape Matching. - Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. - Yuzhe Yang, Zhi Xu:
Rethinking the Value of Labels for Improving Class-Imbalanced Learning. - Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh:
Provably Robust Metric Learning. - Yu Chen, Lingfei Wu, Mohammed J. Zaki:
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. - Yihe Dong, Will Sawin:
COPT: Coordinated Optimal Transport on Graphs. - Nimit Sharad Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré:
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems. - Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang:
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. - Lang Huang, Chao Zhang, Hongyang Zhang:
Self-Adaptive Training: beyond Empirical Risk Minimization. - Jonathan Lacotte, Mert Pilanci:
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization. - Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Near-Optimal Comparison Based Clustering. - Xin Liu, Josh Fromm, Shwetak N. Patel, Daniel McDuff:
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement. - Diogo Carvalho, Francisco S. Melo, Pedro Santos:
A new convergent variant of Q-learning with linear function approximation. - Chun-Hsing Lin, Siang-Ruei Wu, Hung-yi Lee, Yun-Nung Chen:
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation. - Gal Vardi, Ohad Shamir:
Neural Networks with Small Weights and Depth-Separation Barriers. - Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie:
Untangling tradeoffs between recurrence and self-attention in artificial neural networks. - Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
Dual-Free Stochastic Decentralized Optimization with Variance Reduction. - Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness:
Online Learning in Contextual Bandits using Gated Linear Networks. - Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal:
Throughput-Optimal Topology Design for Cross-Silo Federated Learning. - Amir Dib:
Quantized Variational Inference. - Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. - Linnan Wang, Rodrigo Fonseca, Yuandong Tian:
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. - Sean Kulinski, Saurabh Bagchi, David I. Inouye:
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests. - Jinseok Kim, Kyungsu Kim, Jae-Joon Kim:
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks. - Allan Jabri, Andrew Owens, Alexei A. Efros:
Space-Time Correspondence as a Contrastive Random Walk. - Adam D. Smith, Shuang Song, Abhradeep Thakurta:
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. - Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme:
Exponential ergodicity of mirror-Langevin diffusions. - Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran:
An Efficient Framework for Clustered Federated Learning. - Harald Steck:
Autoencoders that don't overfit towards the Identity. - Gabriele Farina, Tuomas Sandholm:
Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond. - Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang:
Parameterized Explainer for Graph Neural Network. - Minyoung Kim, Vladimir Pavlovic:
Recursive Inference for Variational Autoencoders. - Jeffrey P. Spence:
Flexible mean field variational inference using mixtures of non-overlapping exponential families. - Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana:
HYDRA: Pruning Adversarially Robust Neural Networks. - Arash Vahdat, Jan Kautz:
NVAE: A Deep Hierarchical Variational Autoencoder. - Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang:
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory. - Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? - Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He:
Counterfactual Prediction for Bundle Treatment. - Hongyu Ren, Jure Leskovec:
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. - Robin Quessard, Thomas D. Barrett, William R. Clements:
Learning Disentangled Representations and Group Structure of Dynamical Environments. - Yingcong Tan, Daria Terekhov, Andrew Delong:
Learning Linear Programs from Optimal Decisions. - Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Ahmed K. Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav V. Marathe:
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. - Rickard Brüel Gabrielsson:
Universal Function Approximation on Graphs. - Steven Dalton, Iuri Frosio:
Accelerating Reinforcement Learning through GPU Atari Emulation. - Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi:
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning. - Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo:
Comparator-Adaptive Convex Bandits. - Jianzhun Du, Joseph Futoma, Finale Doshi-Velez:
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. - Ron Kupfer, Sharon Qian, Eric Balkanski, Yaron Singer:
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation. - Alexander Moreno, Zhenke Wu, Jamie Yap, Cho Lam, David W. Wetter, Inbal Nahum-Shani, Walter H. Dempsey, James M. Rehg:
A Robust Functional EM Algorithm for Incomplete Panel Count Data. - Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang:
Graph Stochastic Neural Networks for Semi-supervised Learning. - Dat Huynh, Ehsan Elhamifar:
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition. - Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake:
A Benchmark for Systematic Generalization in Grounded Language Understanding. - Yutong Wang, Clayton Scott:
Weston-Watkins Hinge Loss and Ordered Partitions. - Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas:
Reinforcement Learning with Augmented Data. - Yi Tian, Jian Qian, Suvrit Sra:
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. - Runzhong Wang, Junchi Yan, Xiaokang Yang:
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning. - Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan:
Estimating Training Data Influence by Tracing Gradient Descent. - Yuandong Tian, Qucheng Gong, Yu Jiang:
Joint Policy Search for Multi-agent Collaboration with Imperfect Information. - Lin Yang, Mohammad Hassan Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong:
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm. - Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang:
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective. - Soham De, Samuel L. Smith:
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks. - Shangtong Zhang, Vivek Veeriah, Shimon Whiteson:
Learning Retrospective Knowledge with Reverse Reinforcement Learning. - Michael Cogswell, Jiasen Lu, Rishabh Jain, Stefan Lee, Devi Parikh, Dhruv Batra:
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data. - Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj K. Singh:
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs. - Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina:
A General Large Neighborhood Search Framework for Solving Integer Linear Programs. - Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. - Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar:
OrganITE: Optimal transplant donor organ offering using an individual treatment effect. - Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su:
The Complete Lasso Tradeoff Diagram. - Emmanuel Abbe, Colin Sandon:
On the universality of deep learning. - Ahmed Zaoui, Christophe Denis, Mohamed Hebiri:
Regression with reject option and application to kNN. - Étienne Bamas, Andreas Maggiori, Ola Svensson:
The Primal-Dual method for Learning Augmented Algorithms. - Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. - Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. - Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu:
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms. - Sait Cakmak, Raul Astudillo, Peter I. Frazier, Enlu Zhou:
Bayesian Optimization of Risk Measures. - Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul M. Zimmerman:
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search. - Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger:
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. - Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang:
PIE-NET: Parametric Inference of Point Cloud Edges. - Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher:
A Simple Language Model for Task-Oriented Dialogue. - Fan Wu, Patrick Rebeschini:
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. - Ian Waudby-Smith, Aaditya Ramdas:
Confidence sequences for sampling without replacement. - Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna:
A mean-field analysis of two-player zero-sum games. - Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant:
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge. - Stephen McAleer, John B. Lanier, Roy Fox, Pierre Baldi:
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games. - Yuqing Zhu, Yu-Xiang Wang:
Improving Sparse Vector Technique with Renyi Differential Privacy. - Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander M. Rush:
Latent Template Induction with Gumbel-CRFs. - Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
Instance Based Approximations to Profile Maximum Likelihood. - Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang:
Factorizable Graph Convolutional Networks. - Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R.:
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses. - Colin White, Willie Neiswanger, Sam Nolen, Yash Savani:
A Study on Encodings for Neural Architecture Search. - Yaochen Xie, Zhengyang Wang, Shuiwang Ji:
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising. - Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda:
Early-Learning Regularization Prevents Memorization of Noisy Labels. - Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia:
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond. - Amit Daniely, Eran Malach:
Learning Parities with Neural Networks. - Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan:
Consistent Plug-in Classifiers for Complex Objectives and Constraints. - Victor Sanh, Thomas Wolf, Alexander M. Rush:
Movement Pruning: Adaptive Sparsity by Fine-Tuning. - Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee:
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. - Mark Herbster, Stephen Pasteris, Lisa Tse:
Online Matrix Completion with Side Information. - Jangho Kim, KiYoon Yoo, Nojun Kwak:
Position-based Scaled Gradient for Model Quantization and Pruning. - Avrim Blum, Han Shao:
Online Learning with Primary and Secondary Losses. - Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec:
Graph Information Bottleneck. - Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. - Dheeraj Baby, Yu-Xiang Wang:
Adaptive Online Estimation of Piecewise Polynomial Trends. - Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain:
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. - Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus:
Agnostic Learning with Multiple Objectives. - Benjamin Biggs, David Novotný, Sébastien Ehrhardt, Hanbyul Joo, Benjamin Graham, Andrea Vedaldi:
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data. - Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin:
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation. - Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k with Optimal Transport. - Ricardo Luna Gutierrez, Matteo Leonetti:
Information-theoretic Task Selection for Meta-Reinforcement Learning. - Roi Livni, Shay Moran:
A Limitation of the PAC-Bayes Framework. - Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. - Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. - João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska:
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay. - Baptiste Rozière, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample:
Unsupervised Translation of Programming Languages. - Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang:
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation. - Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco J. Marmolejo Cossío, Ninad Rajgopal, Alexandros A. Voudouris:
Optimally Deceiving a Learning Leader in Stackelberg Games. - Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman:
Online Optimization with Memory and Competitive Control. - Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. - Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky:
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation. - Jun Hyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, Jinwoo Shin:
Learning from Failure: De-biasing Classifier from Biased Classifier. - Zhisheng Xiao, Qing Yan, Yali Amit:
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. - Sung Woo Park, Dong Wook Shu, Junseok Kwon:
Deep Diffusion-Invariant Wasserstein Distributional Classification. - Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak:
Finding All $\epsilon$-Good Arms in Stochastic Bandits. - Elias Najarro, Sebastian Risi:
Meta-Learning through Hebbian Plasticity in Random Networks. - Mark Bun:
A Computational Separation between Private Learning and Online Learning. - Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen:
Top-KAST: Top-K Always Sparse Training. - Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee:
Meta-Learning with Adaptive Hyperparameters. - Noah Golowich, Sarath Pattathil, Constantinos Daskalakis:
Tight last-iterate convergence rates for no-regret learning in multi-player games. - Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang:
Curvature Regularization to Prevent Distortion in Graph Embedding. - Nathan Inkawhich, Kevin J. Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen:
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability. - Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. - Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. - Guangda Ji, Zhanxing Zhu:
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. - Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou:
Adversarial Attacks on Deep Graph Matching. - Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher:
The Generalization-Stability Tradeoff In Neural Network Pruning. - Yayi Zou, Xiaoqi Lu:
Gradient-EM Bayesian Meta-Learning. - Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. - Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik:
Linearly Converging Error Compensated SGD. - David Novotný, Roman Shapovalov, Andrea Vedaldi:
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction. - Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh:
A Self-Tuning Actor-Critic Algorithm. - Teerapat Jenrungrot, Vivek Jayaram, Steven M. Seitz, Ira Kemelmacher-Shlizerman:
The Cone of Silence: Speech Separation by Localization. - Noémie Jaquier, Leonel Dario Rozo:
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. - Yushi Qiu, Reiji Suda:
Train-by-Reconnect: Decoupling Locations of Weights from Their Values. - Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley:
Learning discrete distributions: user vs item-level privacy. - Yuxuan Zhao, Madeleine Udell:
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula. - André F. T. Martins, António Farinhas, Marcos V. Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo:
Sparse and Continuous Attention Mechanisms. - Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang:
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. - Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu:
Learning by Minimizing the Sum of Ranked Range. - Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane S. Boning, Cho-Jui Hsieh:
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations. - Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang:
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features. - Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. - Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma:
Self-training Avoids Using Spurious Features Under Domain Shift. - Soumya Banerjee:
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions. - Woohyeon Shim, Minsu Cho:
CircleGAN: Generative Adversarial Learning across Spherical Circles. - Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for ℓp-Sampling Without Replacement. - Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park:
Hypersolvers: Toward Fast Continuous-Depth Models. - Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko:
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment. - Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
Escaping the Gravitational Pull of Softmax. - Kaito Ariu, Narae Ryu, Se-Young Yun, Alexandre Proutière:
Regret in Online Recommendation Systems. - Poorya Mianjy, Raman Arora:
On Convergence and Generalization of Dropout Training. - Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari:
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking. - Noam Razin, Nadav Cohen:
Implicit Regularization in Deep Learning May Not Be Explainable by Norms. - Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min:
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. - Subhabrata Mukherjee, Ahmed Hassan Awadallah:
Uncertainty-aware Self-training for Few-shot Text Classification. - Jack Lindsey, Ashok Litwin-Kumar:
Learning to Learn with Feedback and Local Plasticity. - Qi Chen, Lin Sun, Ernest Cheung, Alan L. Yuille:
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization. - Yunwen Lei, Antoine Ledent, Marius Kloft:
Sharper Generalization Bounds for Pairwise Learning. - Junhyung Park, Krikamol Muandet:
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. - Nian Si, Jose H. Blanchet, Soumyadip Ghosh, Mark S. Squillante:
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality. - Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko:
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. - Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu-Hong Hoi, Weinan E:
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning. - Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois C. Knoll:
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor. - Kaizheng Wang, Yuling Yan, Mateo Díaz:
Efficient Clustering for Stretched Mixtures: Landscape and Optimality. - Shuxiao Chen, Edgar Dobriban, Jane H. Lee:
A Group-Theoretic Framework for Data Augmentation. - Raphael A. Meyer, Christopher Musco:
The Statistical Cost of Robust Kernel Hyperparameter Turning. - Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe, Xiaowei Huang:
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? - Minguk Kang, Jaesik Park:
ContraGAN: Contrastive Learning for Conditional Image Generation. - Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu:
On the distance between two neural networks and the stability of learning. - Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris N. Metaxas, Chao Chen:
A Topological Filter for Learning with Label Noise. - Canh T. Dinh, Nguyen Hoang Tran, Tuan Dung Nguyen:
Personalized Federated Learning with Moreau Envelopes. - Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli:
Avoiding Side Effects in Complex Environments. - Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang:
No-regret Learning in Price Competitions under Consumer Reference Effects. - David Alvarez-Melis, Nicolò Fusi:
Geometric Dataset Distances via Optimal Transport. - Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams:
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. - Paris Giampouras, René Vidal, Athanasios A. Rontogiannis, Benjamin D. Haeffele:
A novel variational form of the Schatten-$p$ quasi-norm. - Weitang Liu, Xiaoyun Wang, John D. Owens, Yixuan Li:
Energy-based Out-of-distribution Detection. - Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk:
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. - Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. - Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam:
Active Structure Learning of Causal DAGs via Directed Clique Trees. - Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. - Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. - Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin:
Reconsidering Generative Objectives For Counterfactual Reasoning. - Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie:
Robust Federated Learning: The Case of Affine Distribution Shifts. - Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie:
Quantile Propagation for Wasserstein-Approximate Gaussian Processes. - Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen:
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning. - Benjamin Cowley, Jonathan W. Pillow:
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. - Zhanqiu Zhang, Jianyu Cai, Jie Wang:
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. - Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong:
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. - Thomas Limbacher, Robert Legenstein:
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks. - Julian Chibane, Aymen Mir, Gerard Pons-Moll:
Neural Unsigned Distance Fields for Implicit Function Learning. - Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Smoothing. - Apoorv Vyas, Angelos Katharopoulos, François Fleuret:
Fast Transformers with Clustered Attention. - Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, Krunal Patel, Juan Pablo Vielma:
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification. - Kaiyu Yang, Jia Deng:
Strongly Incremental Constituency Parsing with Graph Neural Networks. - Hao Zhu, Chaoyou Fu, Qianyi Wu, Wayne Wu, Chen Qian, Ran He:
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection. - Ping Hu, Stan Sclaroff, Kate Saenko:
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation. - Juhan Bae, Roger B. Grosse:
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. - Yuan Gao, Christian Kroer:
First-Order Methods for Large-Scale Market Equilibrium Computation. - Zijun Gao, Yanjun Han:
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects. - Ye Yuan, Kris Kitani:
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis. - Ayush Jain, Alon Orlitsky:
A General Method for Robust Learning from Batches. - Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing:
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. - Yannis Kalantidis, Mert Bülent Sariyildiz, Noé Pion, Philippe Weinzaepfel, Diane Larlus:
Hard Negative Mixing for Contrastive Learning. - Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. - Christopher Morris, Gaurav Rattan, Petra Mutzel:
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. - Qianqian Ma, Alex Olshevsky:
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion. - Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha:
Learning Semantic-aware Normalization for Generative Adversarial Networks. - Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin:
Differentiable Causal Discovery from Interventional Data. - Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song:
One-sample Guided Object Representation Disassembling. - Akifumi Okuno, Hidetoshi Shimodaira:
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate. - Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur:
Robust Persistence Diagrams using Reproducing Kernels. - Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour:
Contextual Games: Multi-Agent Learning with Side Information. - Amina Mollaysa, Brooks Paige, Alexandros Kalousis:
Goal-directed Generation of Discrete Structures with Conditional Generative Models. - Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge:
Beyond Lazy Training for Over-parameterized Tensor Decomposition. - Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter:
Denoised Smoothing: A Provable Defense for Pretrained Classifiers. - Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi:
Minibatch Stochastic Approximate Proximal Point Methods. - Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata:
Attribute Prototype Network for Zero-Shot Learning. - Carl Doersch, Ankush Gupta, Andrew Zisserman:
CrossTransformers: spatially-aware few-shot transfer. - Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. - Mark S. Veillette, Siddharth Samsi, Christopher J. Mattioli:
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology. - Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz:
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation. - Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy:
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. - Sidak Pal Singh, Martin Jaggi:
Model Fusion via Optimal Transport. - Kaiqing Zhang, Bin Hu, Tamer Basar:
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems. - Ziluo Ding, Tiejun Huang, Zongqing Lu:
Learning Individually Inferred Communication for Multi-Agent Cooperation. - Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman:
Set2Graph: Learning Graphs From Sets. - Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang:
Graph Random Neural Networks for Semi-Supervised Learning on Graphs. - Robert A. Giaquinto, Arindam Banerjee:
Gradient Boosted Normalizing Flows. - Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec:
Open Graph Benchmark: Datasets for Machine Learning on Graphs. - Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. - Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen Cagli:
Texture Interpolation for Probing Visual Perception. - Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, Zongyuan Ge:
Hierarchical Neural Architecture Search for Deep Stereo Matching. - Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun:
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models. - Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry:
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. - Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori:
Focus of Attention Improves Information Transfer in Visual Features. - Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? - Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden:
A Dynamical Central Limit Theorem for Shallow Neural Networks. - Nicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher Pal:
Measuring Systematic Generalization in Neural Proof Generation with Transformers. - Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. - Clayton Scott, Jianxin Zhang:
Learning from Label Proportions: A Mutual Contamination Framework. - Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. - Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory G. Slabaugh, Qi Tian:
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs. - Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. - Gavin Smith, Roberto Mansilla, James Goulding:
Model Class Reliance for Random Forests. - Arun Sai Suggala, Praneeth Netrapalli:
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. - Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang:
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. - Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang:
Learning to Adapt to Evolving Domains. - Umair Z. Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish Singla:
Synthesizing Tasks for Block-based Programming. - Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen:
Scalable Belief Propagation via Relaxed Scheduling. - Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu:
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. - Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie:
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret. - Salman Habib, Allison Beemer, Jörg Kliewer:
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes. - Heinrich Jiang, Jennifer Jang, Jakub Lacki:
Faster DBSCAN via subsampled similarity queries. - Zhen Sun, Roei Schuster, Vitaly Shmatikov:
De-Anonymizing Text by Fingerprinting Language Generation. - Mathieu Carrière, Andrew J. Blumberg:
Multiparameter Persistence Image for Topological Machine Learning. - Raphaël Dang-Nhu:
PLANS: Neuro-Symbolic Program Learning from Videos. - Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher:
Matrix Inference and Estimation in Multi-Layer Models. - Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur M. Bagautdinov, Pierre Baqué, Pascal Fua:
MeshSDF: Differentiable Iso-Surface Extraction. - HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim:
Variational Interaction Information Maximization for Cross-domain Disentanglement. - Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang:
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. - Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira, William W. Cohen:
Faithful Embeddings for Knowledge Base Queries. - Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao:
Wasserstein Distances for Stereo Disparity Estimation. - Nitin Kamra, Hao Zhu, Dweep Trivedi, Ming Zhang, Yan Liu:
Multi-agent Trajectory Prediction with Fuzzy Query Attention. - Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Günlük:
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping. - Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia:
An Analysis of SVD for Deep Rotation Estimation. - Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz:
Can the Brain Do Backpropagation? - Exact Implementation of Backpropagation in Predictive Coding Networks. - Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin:
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. - Ilai Bistritz, Ariana J. Mann, Nicholas Bambos:
Distributed Distillation for On-Device Learning. - Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, Thomas Brox:
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning. - Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu:
Passport-aware Normalization for Deep Model Protection. - Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen:
Sampling-Decomposable Generative Adversarial Recommender. - Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua:
Limits to Depth Efficiencies of Self-Attention.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-09-23 23:31 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
