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ORCID35th ICML 2018: Stockholm, Sweden
- Jennifer G. Dy, Andreas Krause:
Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018. Proceedings of Machine Learning Research 80, PMLR 2018 - Marc Abeille, Alessandro Lazaric:
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems. 1-9 - David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman:
State Abstractions for Lifelong Reinforcement Learning. 10-19 - David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman:
Policy and Value Transfer in Lifelong Reinforcement Learning. 20-29 - Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang:
INSPECTRE: Privately Estimating the Unseen. 30-39 - Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas:
Learning Representations and Generative Models for 3D Point Clouds. 40-49 - Tameem Adel, Zoubin Ghahramani, Adrian Weller:
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. 50-59 - Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. 60-69 - Arpit Agarwal, Prathamesh Patil, Shivani Agarwal:
Accelerated Spectral Ranking. 70-79 - Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. 80-88 - Raj Agrawal, Caroline Uhler, Tamara Broderick:
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. 89-98 - Shipra Agrawal, Morteza Zadimoghaddam, Vahab S. Mirrokni:
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy. 99-108 - Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin:
Bucket Renormalization for Approximate Inference. 109-118 - Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox:
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis. 119-128 - Ahmed M. Alaa, Mihaela van der Schaar:
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. 129-138 - Ahmed M. Alaa, Mihaela van der Schaar:
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning. 139-148 - Ibrahim M. Alabdulmohsin:
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization. 149-158 - Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy:
Fixing a Broken ELBO. 159-168 - Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld:
Differentially Private Identity and Equivalence Testing of Discrete Distributions. 169-178 - Zeyuan Allen-Zhu:
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization. 179-185 - Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li:
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. 186-194 - Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville:
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. 195-204 - Ron Amit, Ron Meir:
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory. 205-214 - Matthew Amodio, Smita Krishnaswamy:
MAGAN: Aligning Biological Manifolds. 215-223 - Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong:
Subspace Embedding and Linear Regression with Orlicz Norm. 224-233 - Oleg Arenz
, Mingjun Zhong, Gerhard Neumann:
Efficient Gradient-Free Variational Inference using Policy Search. 234-243 - Sanjeev Arora, Nadav Cohen, Elad Hazan:
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. 244-253 - Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger Generalization Bounds for Deep Nets via a Compression Approach. 254-263 - Kavosh Asadi, Dipendra Misra, Michael L. Littman:
Lipschitz Continuity in Model-based Reinforcement Learning. 264-273 - Anish Athalye, Nicholas Carlini, David A. Wagner:
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. 274-283 - Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok:
Synthesizing Robust Adversarial Examples. 284-293 - Pranjal Awasthi, Aravindan Vijayaraghavan:
Clustering Semi-Random Mixtures of Gaussians. 294-303 - Davide Bacciu, Federico Errica, Alessio Micheli:
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. 304-313 - Wenruo Bai, Jeffrey A. Bilmes:
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions. 314-323 - Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. 324-333 - Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang:
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions. 334-343 - Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst:
A Boo(n) for Evaluating Architecture Performance. 344-352 - Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik:
Learning to Branch. 353-362 - David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. 363-372 - Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard G. Baraniuk:
Spline Filters For End-to-End Deep Learning. 373-382 - Randall Balestriero, Richard G. Baraniuk:
A Spline Theory of Deep Networks. 383-392 - Eric Balkanski, Yaron Singer:
Approximation Guarantees for Adaptive Sampling. 393-402 - Borja Balle, Yu-Xiang Wang:
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. 403-412 - Lukas Balles, Philipp Hennig:
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. 413-422 - Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf:
Differentially Private Database Release via Kernel Mean Embeddings. 423-431 - Robert Bamler, Stephan Mandt:
Improving Optimization in Models With Continuous Symmetry Breaking. 432-441 - Duhyeon Bang, Hyunjung Shim:
Improved Training of Generative Adversarial Networks using Representative Features. 442-451 - Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya:
Using Inherent Structures to design Lean 2-layer RBMs. 452-460 - Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. 461-470 - Ricardo Baptista, Matthias Poloczek:
Bayesian Optimization of Combinatorial Structures. 471-480 - Pierre Baqué, Edoardo Remelli, François Fleuret, Pascal Fua:
Geodesic Convolutional Shape Optimization. 481-490 - Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt:
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems. 491-499 - Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal:
Testing Sparsity over Known and Unknown Bases. 500-509 - André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. 510-519 - Peter L. Bartlett, David P. Helmbold, Philip M. Long:
Gradient descent with identity initialization efficiently learns positive definite linear transformations. 520-529 - Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville:
Mutual Information Neural Estimation. 530-539 - Mikhail Belkin, Siyuan Ma, Soumik Mandal:
To Understand Deep Learning We Need to Understand Kernel Learning. 540-548 - Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc V. Le:
Understanding and Simplifying One-Shot Architecture Search. 549-558 - Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar:
SIGNSGD: Compressed Optimisation for Non-Convex Problems. 559-568 - Aditya Bhaskara, Maheshakya Wijewardena:
Distributed Clustering via LSH Based Data Partitioning. 569-578 - Mikolaj Binkowski, Gautier Marti, Philippe Donnat:
Autoregressive Convolutional Neural Networks for Asynchronous Time Series. 579-588 - Guy Blanc, Steffen Rendle:
Adaptive Sampled Softmax with Kernel Based Sampling. 589-598 - Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam:
Optimizing the Latent Space of Generative Networks. 599-608 - Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. 609-618 - Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang:
A Progressive Batching L-BFGS Method for Machine Learning. 619-628 - Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty:
Prediction Rule Reshaping. 629-637 - Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò:
QuantTree: Histograms for Change Detection in Multivariate Data Streams. 638-647 - Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang:
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. 648-657 - Nataly Brukhim, Amir Globerson:
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction. 658-666 - Alexander Buchholz, Florian Wenzel, Stephan Mandt:
Quasi-Monte Carlo Variational Inference. 667-676 - Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu:
Path-Level Network Transformation for Efficient Architecture Search. 677-686 - Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko:
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. 687-696 - Trevor Campbell, Tamara Broderick:
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. 697-705 - Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan:
Adversarial Learning with Local Coordinate Coding. 706-714 - L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi:
Fair and Diverse DPP-Based Data Summarization. 715-724 - Ciwan Ceylan, Michael U. Gutmann:
Conditional Noise-Contrastive Estimation of Unnormalised Models. 725-733 - Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Alan Goldstein, Lawrence Carin, Ricardo Henao:
Adversarial Time-to-Event Modeling. 734-743 - Zachary Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. 744-753 - Satrajit Chatterjee:
Learning and Memorization. 754-762 - Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. 763-772 - Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar:
Hierarchical Clustering with Structural Constraints. 773-782 - Zhengping Che, Sanjay Purushotham, Max Guangyu Li, Bo Jiang, Yan Liu:
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series. 783-792 - Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich:
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. 793-802 - Lin Chen, Moran Feldman, Amin Karbasi:
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? 803-812 - Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi:
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity. 813-822 - Changyou Chen, Chunyuan Li, Liquan Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin:
Continuous-Time Flows for Efficient Inference and Density Estimation. 823-832 - Yichen Chen, Lihong Li, Mengdi Wang:
Scalable Bilinear Learning Using State and Action Features. 833-842 - Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. 843-852 - Ting Chen, Martin Renqiang Min, Yizhou Sun:
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations. 853-862 - Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel:
PixelSNAIL: An Improved Autoregressive Generative Model. 863-871 - Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz:
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. 872-881 - Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. 882-891 - Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin:
Variational Inference and Model Selection with Generalized Evidence Bounds. 892-901 - Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. 902-911 - Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang:
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. 912-920 - Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu:
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. 921-930 - Di Chen, Yexiang Xue, Carla P. Gomes:
End-to-End Learning for the Deep Multivariate Probit Model. 931-940 - Jianfei Chen, Jun Zhu, Le Song:
Stochastic Training of Graph Convolutional Networks with Variance Reduction. 941-949 - Minhao Cheng, Ian Davidson, Cho-Jui Hsieh:
Extreme Learning to Rank via Low Rank Assumption. 950-959 - Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Learning a Mixture of Two Multinomial Logits. 960-968 - Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
Structured Evolution with Compact Architectures for Scalable Policy Optimization. 969-977 - Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. 978-987 - Agniva Chowdhury, Jiasen Yang, Petros Drineas:
An Iterative, Sketching-based Framework for Ridge Regression. 988-997 - Sebastian Claici, Edward Chien, Justin Solomon:
Stochastic Wasserstein Barycenters. 998-1007 - John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine:
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. 1008-1017 - Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia:
On Acceleration with Noise-Corrupted Gradients. 1018-1027 - Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar:
Online Linear Quadratic Control. 1028-1037 - Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer:
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms. 1038-1047 - Graham Cormode, Charlie Dickens, David P. Woodruff:
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms. 1048-1056 - Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. 1057-1066 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang:
Online Learning with Abstention. 1067-1075 - Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya R. Gupta, Jeff A. Bilmes:
Constrained Interacting Submodular Groupings. 1076-1085 - Chris Cremer, Xuechen Li, David Duvenaud:
Inference Suboptimality in Variational Autoencoders. 1086-1094 - Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu:
Mix & Match Agent Curricula for Reinforcement Learning. 1095-1103 - Will Dabney, Georg Ostrovski, David Silver, Rémi Munos:
Implicit Quantile Networks for Distributional Reinforcement Learning. 1104-1113 - Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song:
Learning Steady-States of Iterative Algorithms over Graphs. 1114-1122 - Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song:
Adversarial Attack on Graph Structured Data. 1123-1132 - Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song:
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. 1133-1142 - Bin Dai, Chen Zhu, Baining Guo, David P. Wipf:
Compressing Neural Networks using the Variational Information Bottleneck. 1143-1152 - Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki:
Asynchronous Byzantine Machine Learning (the case of SGD). 1153-1162 - Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. 1163-1172 - Christopher De Sa, Vincent Chen, Wing Wong:
Minibatch Gibbs Sampling on Large Graphical Models. 1173-1181 - Emily Denton, Rob Fergus:
Stochastic Video Generation with a Learned Prior. 1182-1191 - Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. 1192-1201 - Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy:
Accurate Inference for Adaptive Linear Models. 1202-1211 - Amir Dezfouli, Edwin V. Bonilla, Richard Nock:
Variational Network Inference: Strong and Stable with Concrete Support. 1212-1221 - Manik Dhar, Aditya Grover, Stefano Ermon:
Modeling Sparse Deviations for Compressed Sensing using Generative Models. 1222-1231 - Jelena Diakonikolas, Lorenzo Orecchia:
Alternating Randomized Block Coordinate Descent. 1232-1240 - Jilles Steeve Dibangoye, Olivier Buffet:
Learning to Act in Decentralized Partially Observable MDPs. 1241-1250