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ORCID10th ICLR 2022: Virtual Event
- The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net 2022
Oral Presentations
- Sabri Eyuboglu, Maya Varma, Khaled Kamal Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré:
Domino: Discovering Systematic Errors with Cross-Modal Embeddings. - Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, Jacob Andreas:
Natural Language Descriptions of Deep Visual Features. - Lixu Wang, Shichao Xu, Ruiqi Xu, Xiao Wang, Qi Zhu:
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization. - Meng Qu, Huiyu Cai, Jian Tang:
Neural Structured Prediction for Inductive Node Classification. - Asiri Wijesinghe, Qing Wang:
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?". - Chulhee Yun, Shashank Rajput, Suvrit Sra:
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. - Yifei Wang, Jonathan Lacotte, Mert Pilanci:
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions. - Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. - Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh:
Bootstrapped Meta-Learning. - Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, Yoshua Bengio:
Coordination Among Neural Modules Through a Shared Global Workspace. - Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik:
Data-Efficient Graph Grammar Learning for Molecular Generation. - Nicholas Carlini, Andreas Terzis:
Poisoning and Backdooring Contrastive Learning. - X. Y. Han, Vardan Papyan, David L. Donoho:
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path. - Shuxiao Chen, Koby Crammer, Hangfeng He
, Dan Roth, Weijie J. Su:
Weighted Training for Cross-Task Learning. - Marine Schimel, Ta-Chu Kao, Kristopher T. Jensen, Guillaume Hennequin:
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. - Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang:
Extending the WILDS Benchmark for Unsupervised Adaptation. - S. Chandra Mouli, Bruno Ribeiro:
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks. - Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon:
Comparing Distributions by Measuring Differences that Affect Decision Making. - Yusong Wu, Ethan Manilow, Yi Deng, Rigel Swavely, Kyle Kastner, Tim Cooijmans, Aaron C. Courville, Cheng-Zhi Anna Huang, Jesse H. Engel:
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling. - Bo Wan, Wenjuan Han, Zilong Zheng, Tinne Tuytelaars:
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. - Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao:
PiCO: Contrastive Label Disambiguation for Partial Label Learning. - Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar:
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. - Floris Geerts, Juan L. Reutter:
Expressiveness and Approximation Properties of Graph Neural Networks. - Steeven Janny, Fabien Baradel, Natalia Neverova, Madiha Nadri, Greg Mori, Christian Wolf:
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space. - Hangbo Bao, Li Dong, Songhao Piao, Furu Wei:
BEiT: BERT Pre-Training of Image Transformers. - Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, Percy Liang:
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. - Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski:
StyleAlign: Analysis and Applications of Aligned StyleGAN Models. - Kohei Miyaguchi, Takayuki Katsuki, Akira Koseki, Toshiya Iwamori:
Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion. - Albert Gu, Karan Goel, Christopher Ré:
Efficiently Modeling Long Sequences with Structured State Spaces. - Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto:
Large Language Models Can Be Strong Differentially Private Learners. - Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang:
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. - Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, Yaron Lipman:
Frame Averaging for Invariant and Equivariant Network Design. - Alex Rogozhnikov:
Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation. - Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, Ali Taylan Cemgil:
A Fine-Grained Analysis on Distribution Shift. - Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman:
Open-Set Recognition: A Good Closed-Set Classifier is All You Need. - Rachid Riad, Olivier Teboul, David Grangier, Neil Zeghidour:
Learning Strides in Convolutional Neural Networks. - Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. - Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Sergeevich Kudinov, Jiansheng Wei:
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme. - Shuming Kong, Yanyan Shen, Linpeng Huang:
Resolving Training Biases via Influence-based Data Relabeling. - Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang:
Representational Continuity for Unsupervised Continual Learning. - Kyle Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, Chelsea Finn:
Vision-Based Manipulators Need to Also See from Their Hands. - Huaxiu Yao, Linjun Zhang, Chelsea Finn:
Meta-Learning with Fewer Tasks through Task Interpolation. - Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang:
Discovering and Explaining the Representation Bottleneck of DNNS. - António Farinhas, Wilker Aziz, Vlad Niculae, André F. T. Martins:
Sparse Communication via Mixed Distributions. - Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le:
Finetuned Language Models are Zero-Shot Learners. - Qing Jin, Jian Ren, Richard Zhuang, Sumant Hanumante, Zhengang Li, Zhiyu Chen, Yanzhi Wang, Kaiyuan Yang, Sergey Tulyakov:
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization. - Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris M. Kitani:
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. - Boris N. Oreshkin, Florent Bocquelet, Félix G. Harvey, Bay Raitt, Dominic Laflamme:
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics. - Nicolas Papernot, Thomas Steinke:
Hyperparameter Tuning with Renyi Differential Privacy. - Mia Chiquier, Chengzhi Mao, Carl Vondrick:
Real-Time Neural Voice Camouflage. - Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo:
CycleMLP: A MLP-like Architecture for Dense Prediction. - Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang:
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models. - Pingchuan Ma, Tao Du, Joshua B. Tenenbaum, Wojciech Matusik, Chuang Gan:
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation. - Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine:
The Information Geometry of Unsupervised Reinforcement Learning. - Rose E. Wang, Esin Durmus, Noah D. Goodman, Tatsunori Hashimoto:
Language modeling via stochastic processes.
Poster Presentations
- Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, Daniel C. Alexander:
Learning to Downsample for Segmentation of Ultra-High Resolution Images. - Rasmus Berg Palm, Miguel González Duque, Shyam Sudhakaran, Sebastian Risi:
Variational Neural Cellular Automata. - Todor Davchev, Oleg Olegovich Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz:
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation. - Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger:
L0-Sparse Canonical Correlation Analysis. - Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu, Christopher G. Brinton:
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? - Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang:
Is Homophily a Necessity for Graph Neural Networks? - Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu:
DEGREE: Decomposition Based Explanation for Graph Neural Networks. - Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. - Xueyuan She, Saurabh Dash, Saibal Mukhopadhyay:
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods. - Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff A. Bilmes:
Diverse Client Selection for Federated Learning via Submodular Maximization. - Da Xu, Yuting Ye, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan:
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation. - Alexey Zakharov, Qinghai Guo, Zafeirios Fountas:
Variational Predictive Routing with Nested Subjective Timescales. - Jia Guo, Jiankang Deng, Alexandros Lattas, Stefanos Zafeiriou:
Sample and Computation Redistribution for Efficient Face Detection. - Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu:
Sound Adversarial Audio-Visual Navigation. - Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath:
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. - Junfeng Guo, Ang Li, Cong Liu:
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis. - Kirby Banman, Liam Peet-Pare, Nidhi Hegde, Alona Fyshe, Martha White:
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum. - Chirag Gupta, Aaditya Ramdas:
Top-label calibration and multiclass-to-binary reductions. - Gabriel Mel, Jeffrey Pennington:
Anisotropic Random Feature Regression in High Dimensions. - Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash:
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. - Alberto Bietti:
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective. - Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander Toshev, Sergey Levine, Brian Ichter:
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning. - Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. - Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton:
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. - Seng Pei Liew, Tsubasa Takahashi, Michihiko Ueno:
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning. - Michelle Miller, SueYeon Chung, Kenneth D. Miller:
Divisive Feature Normalization Improves Image Recognition Performance in AlexNet. - Benjamin LeBrun, Alessandro Sordoni, Timothy J. O'Donnell:
Evaluating Distributional Distortion in Neural Language Modeling. - Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. - Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. - Siyan Liu, Pei Zhang, Dan Lu, Guannan Zhang:
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks. - Yingzhen Yang, Ping Li:
Discriminative Similarity for Data Clustering. - Yuqing Du, Pieter Abbeel, Aditya Grover:
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation. - Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li:
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing. - Liming Pan, Cheng Shi, Ivan Dokmanic:
Neural Link Prediction with Walk Pooling. - Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. - Yu Meng, Chenyan Xiong, Payal Bajaj, Saurabh Tiwary, Paul N. Bennett, Jiawei Han, Xia Song:
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators. - Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick:
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations. - Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A. Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Understanding and Leveraging Overparameterization in Recursive Value Estimation. - Khashayar Gatmiry, Stefanie Jegelka, Jonathan A. Kelner:
Optimization and Adaptive Generalization of Three layer Neural Networks. - Ruibo Liu, Chongyang Gao, Chenyan Jia, Guangxuan Xu, Soroush Vosoughi:
Non-Parallel Text Style Transfer with Self-Parallel Supervision. - Quanfu Fan, Chun-Fu Chen, Rameswar Panda:
Can an Image Classifier Suffice For Action Recognition? - Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. - Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel:
NeuPL: Neural Population Learning. - Minghao Han, Jacob Euler-Rolle, Robert K. Katzschmann:
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator. - Panagiotis Misiakos, Georgios Smyrnis, George Retsinas, Petros Maragos:
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes. - Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji:
Learning Towards The Largest Margins. - Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan Lin:
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? - David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alexey Kurakin:
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. - Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, Martin T. Vechev:
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. - Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi:
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality. - Abhishek Shetty, Raaz Dwivedi, Lester Mackey:
Distribution Compression in Near-Linear Time. - Frank F. Xu, Junxian He, Graham Neubig, Vincent Josua Hellendoorn:
Capturing Structural Locality in Non-parametric Language Models. - Shaojin Ding, Tianlong Chen, Zhangyang Wang:
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable. - Georgios Georgakis, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Kostas Daniilidis:
Learning to Map for Active Semantic Goal Navigation. - Danijar Hafner:
Benchmarking the Spectrum of Agent Capabilities. - Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka:
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks. - Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham W. Taylor:
On Evaluation Metrics for Graph Generative Models. - Emily Black, Klas Leino, Matt Fredrikson:
Selective Ensembles for Consistent Predictions. - Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah:
Graph Condensation for Graph Neural Networks. - Yonatan Dukler, Alessandro Achille, Giovanni Paolini, Avinash Ravichandran, Marzia Polito, Stefano Soatto:
DIVA: Dataset Derivative of a Learning Task. - Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. - Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick:
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings. - Victor Schmidt, Alexandra Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernández-García, Yoshua Bengio:
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods. - Tracy Ke, Longlin Wang:
A Comparison of Hamming Errors of Representative Variable Selection Methods. - Gabriele Cesa, Leon Lang, Maurice Weiler:
A Program to Build E(N)-Equivariant Steerable CNNs. - Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff:
Minimax Optimization with Smooth Algorithmic Adversaries. - Xiaoyun Li, Belhal Karimi, Ping Li:
On Distributed Adaptive Optimization with Gradient Compression. - Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. - Yutong Wang, Clayton Scott:
VC dimension of partially quantized neural networks in the overparametrized regime. - Yangjun Ruan, Yann Dubois, Chris J. Maddison:
Optimal Representations for Covariate Shift. - Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks.