ICML 2019
766 papers
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
Jingkai Mao, Jakob Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar A Fully Differentiable Beam Search Decoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin Active Embedding Search via Noisy Paired Comparisons
Gregory Canal, Andy Massimino, Mark Davenport, Christopher Rozell Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski Active Learning with Disagreement Graphs
Corinna Cortes, Giulia Desalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile Active Manifolds: A Non-Linear Analogue to Active Subspaces
Robert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoff Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori Adaptive Sensor Placement for Continuous Spaces
James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Bautista Martin, Shih-Yu Sun, Carlos Guestrin, Josh Susskind Adversarial Examples from Computational Constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn Adversarially Learned Representations for Information Obfuscation and Inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapiro Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh Almost Surely Constrained Convex Optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher Amortized Monte Carlo Integration
Adam Golinski, Frank Wood, Tom Rainforth An Investigation of Model-Free Planning
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap Analyzing Federated Learning Through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo Anomaly Detection with Multiple-Hypotheses Predictions
Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox Area Attention
Yang Li, Lukasz Kaiser, Samy Bengio, Si Si AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne Automated Model Selection with Bayesian Quadrature
Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson Band-Limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang Batch Policy Learning Under Constraints
Hoang Le, Cameron Voloshin, Yisong Yue Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro Bayesian Leave-One-Out Cross-Validation for Large Data
Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni Bayesian Optimization Meets Bayesian Optimal Stopping
Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf Bilinear Bandits with Low-Rank Structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak Blended Conditonal Gradients
Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan Calibrated Model-Based Deep Reinforcement Learning
Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon Categorical Feature Compression via Submodular Optimization
Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem Cognitive Model Priors for Predicting Human Decisions
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths Collaborative Channel Pruning for Deep Networks
Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford Combating Label Noise in Deep Learning Using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof Combining Parametric and Nonparametric Models for Off-Policy Evaluation
Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez COMIC: Multi-View Clustering Without Parameter Selection
Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia Composing Entropic Policies Using Divergence Correction
Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess Composing Value Functions in Reinforcement Learning
Benjamin Van Niekerk, Steven James, Adam Earle, Benjamin Rosman Compressing Gradient Optimizers via Count-Sketches
Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava Conditional Independence in Testing Bayesian Networks
Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé Iii, John Langford, Paul Mineiro Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick Convolutional Poisson Gamma Belief Network
Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou Coresets for Ordered Weighted Clustering
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi Counterfactual Visual Explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer Dead-Ends and Secure Exploration in Reinforcement Learning
Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen, Samira Ebrahimi Kahou Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap Deep Counterfactual Regret Minimization
Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare Demystifying Dropout
Hongchang Gao, Jian Pei, Heng Huang Diagnosing Bottlenecks in Deep Q-Learning Algorithms
Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine Differentiable Dynamic Normalization for Learning Deep Representation
Ping Luo, Peng Zhanglin, Shao Wenqi, Zhang Ruimao, Ren Jiamin, Wu Lingyun Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer Dimensionality Reduction for Tukey Regression
Kenneth Clarkson, Ruosong Wang, David Woodruff Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, Jon Kleinberg Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyen Discovering Context Effects from Raw Choice Data
Arjun Seshadri, Alex Peysakhovich, Johan Ugander Disentangled Graph Convolutional Networks
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu Distributed Learning with Sublinear Communication
Jayadev Acharya, Chris De Sa, Dylan Foster, Karthik Sridharan Distribution Calibration for Regression
Hao Song, Tom Diethe, Meelis Kull, Peter Flach Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu DL2: Training and Querying Neural Networks with Logic
Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar Does Data Augmentation Lead to Positive Margin?
Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos Dropout as a Structured Shrinkage Prior
Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels, Diederik Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang Efficient Full-Matrix Adaptive Regularization
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang Efficient Training of BERT by Progressively Stacking
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tieyan Liu ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick EMI: Exploration with Mutual Information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, Gregory Valiant Error Feedback Fixes SignSGD and Other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi Escaping Saddle Points with Adaptive Gradient Methods
Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra Estimating Information Flow in Deep Neural Networks
Ziv Goldfeld, Ewout Van Den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy Exploring the Landscape of Spatial Robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry Fair K-Center Clustering for Data Summarization
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern Fairness Risk Measures
Robert Williamson, Aditya Menon Fairwashing: The Risk of Rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp Fast Context Adaptation via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whiteson Faster Algorithms for Binary Matrix Factorization
Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff Finding Options That Minimize Planning Time
Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, David Lopez-Paz Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon Garbage in, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh Generalized Linear Rule Models
Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Gunluk Generalized Majorization-Minimization
Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb Geometric Losses for Distributional Learning
Arthur Mensch, Mathieu Blondel, Gabriel Peyré GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian Tang Graph Element Networks: Adaptive, Structured Computation and Memory
Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelbling Graph Resistance and Learning from Pairwise Comparisons
Julien Hendrickx, Alexander Olshevsky, Venkatesh Saligrama Graph U-Nets
Hongyang Gao, Shuiwang Ji Greedy Layerwise Learning Can Scale to ImageNet
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern Guided Evolutionary Strategies: Augmenting Random Search with Surrogate Gradients
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein Hessian Aided Policy Gradient
Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi Hierarchical Importance Weighted Autoencoders
Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville Hierarchically Structured Meta-Learning
Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li Hiring Under Uncertainty
Manish Purohit, Sreenivas Gollapudi, Manish Raghavan Homomorphic Sensing
Manolis Tsakiris, Liangzu Peng How Does Disagreement Help Generalization Against Label Corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, Masashi Sugiyama Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai Hybrid Models with Deep and Invertible Features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan IMEXnet a Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto Imitating Latent Policies from Observation
Ashley Edwards, Himanshu Sahni, Yannick Schroecker, Charles Isbell Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama Inference and Sampling of $k_33$-Free Ising Models
Valerii Likhosherstov, Yury Maximov, Misha Chertkov Infinite Mixture Prototypes for Few-Shot Learning
Kelsey Allen, Evan Shelhamer, Hanul Shin, Joshua Tenenbaum Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy, Zaid Harchaoui Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf Ladder Capsule Network
Taewon Jeong, Youngmin Lee, Heeyoung Kim Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip Thomas Learning and Data Selection in Big Datasets
Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund Learning Context-Dependent Label Permutations for Multi-Label Classification
Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz Learning Deep Kernels for Exponential Family Densities
Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Re Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He Learning from a Learner
Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Timothy Arthur Mann, Sven Gowal, Andras Gyorgy, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan Learning Generative Models Across Incomparable Spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka Learning Hawkes Processes Under Synchronization Noise
William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson Learning Novel Policies for Tasks
Yunbo Zhang, Wenhao Yu, Greg Turk Learning Optimal Fair Policies
Razieh Nabi, Daniel Malinsky, Ilya Shpitser Learning to Bid in Revenue-Maximizing Auctions
Thomas Nedelec, Noureddine El Karoui, Vianney Perchet Learning to Clear the Market
Weiran Shen, Sebastien Lahaie, Renato Paes Leme Learning to Collaborate in Markov Decision Processes
Goran Radanovic, Rati Devidze, David Parkes, Adish Singla Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel Learning to Route in Similarity Graphs
Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko Learning to Select for a Predefined Ranking
Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev, Pavel Serdyukov Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin LegoNet: Efficient Convolutional Neural Networks with Lego Filters
Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach Loss Landscapes of Regularized Linear Autoencoders
Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva, Cotton Seed Low Latency Privacy Preserving Inference
Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha Making Decisions That Reduce Discriminatory Impacts
Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio Meta-Learning Neural Bloom Filters
Jack Rae, Sergey Bartunov, Timothy Lillicrap Metric-Optimized Example Weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta Metropolis-Hastings Generative Adversarial Networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan Model Comparison for Semantic Grouping
Francisco Vargas, Kamen Brestnichki, Nils Hammerla Model-Based Active Exploration
Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez Monge Blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle, Zoltan Szabo, Timothée Mathieu, Guillaume Lecue Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner Multi-Objective Training of Generative Adversarial Networks with Multiple Discriminators
Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas Multivariate Submodular Optimization
Richard Santiago, F. Bruce Shepherd Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter Neural Collaborative Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian Neurally-Guided Structure Inference
Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu New Results on Information Theoretic Clustering
Ferdinando Cicalese, Eduardo Laber, Lucas Murtinho Noisy Dual Principal Component Pursuit
Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Rene Vidal, Manolis Tsakiris, Daniel Robinson Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé Iii, Kyunghyun Cho Non-Parametric Priors for Generative Adversarial Networks
Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin Braun Nonparametric Bayesian Deep Networks with Local Competition
Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis Obtaining Fairness Using Optimal Transport Theory
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes On Learning Invariant Representations for Domain Adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordon On Medians of (Randomized) Pairwise Means
Pierre Laforgue, Stephan Clemencon, Patrice Bertail On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama On the Complexity of Approximating Wasserstein Barycenters
Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu On the Design of Estimators for Bandit Off-Policy Evaluation
Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou, Tony Jebara On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne On the Spectral Bias of Neural Networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, George Tucker Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh Online Learning to Rank with Features
Shuai Li, Tor Lattimore, Csaba Szepesvari Online Learning with Kernel Losses
Niladri Chatterji, Aldo Pacchiano, Peter Bartlett Online Learning with Sleeping Experts and Feedback Graphs
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Scott Yang Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine Online Variance Reduction with Mixtures
Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause Open-Ended Learning in Symmetric Zero-Sum Games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel Optimal Auctions Through Deep Learning
Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger Optimal Mini-Batch and Step Sizes for SAGA
Nidham Gazagnadou, Robert Gower, Joseph Salmon Optimal Minimal Margin Maximization with Boosting
Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund Optimal Transport for Structured Data with Application on Graphs
Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, Rémi Flamary Optimistic Policy Optimization via Multiple Importance Sampling
Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli Orthogonal Random Forest for Causal Inference
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen Overcoming Multi-Model Forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat PAC Learnability of Node Functions in Networked Dynamical Systems
Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly Pareto Optimal Streaming Unsupervised Classification
Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo Particle Flow Bayes’ Rule
Xinshi Chen, Hanjun Dai, Le Song Per-Decision Option Discounting
Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowe, Doina Precup Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin POLITEX: Regret Bounds for Policy Iteration Using Expert Prediction
Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, Gellert Weisz POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin Position-Aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec Power K-Means Clustering
Jason Xu, Kenneth Lange Predicate Exchange: Inference with Declarative Knowledge
Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh Projections for Approximate Policy Iteration Algorithms
Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala Provable Guarantees for Gradient-Based Meta-Learning
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest Provably Efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, Luca Daniel Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman RaFM: Rank-Aware Factorization Machines
Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, Jon Mcauliffe Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann Refined Complexity of PCA with Outliers
Kirill Simonov, Fedor Fomin, Petr Golovach, Fahad Panolan Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis Relational Pooling for Graph Representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro Robust Decision Trees Against Adversarial Examples
Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh Robust Learning from Untrusted Sources
Nikola Konstantinov, Christoph Lampert Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet Des Combes SAGA with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik Scalable Fair Clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucet Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena Self-Attention Graph Pooling
Junhyun Lee, Inyeop Lee, Jaewoo Kang Self-Similar Epochs: Value in Arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias Self-Supervised Exploration via Disagreement
Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar Sensitivity Analysis of Linear Structural Causal Models
Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim Separating Value Functions Across Time-Scales
Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau, Yann Ollivier Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart Shape Constraints for Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton Simple Black-Box Adversarial Attacks
Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger Simplifying Graph Convolutional Networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z. Leibo, Nando De Freitas SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin Stable and Fair Classification
Lingxiao Huang, Nisheeth Vishnoi State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer Statistical Foundations of Virtual Democracy
Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oates Stochastic Deep Networks
Gwendoline De Bie, Gabriel Peyré, Marco Cuturi Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat Structured Agents for Physical Construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly Stachenfeld, Pushmeet Kohli, Peter Battaglia, Jessica Hamrick Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu Supervised Hierarchical Clustering with Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallum SWALP : Stochastic Weight Averaging in Low Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Chris De Sa TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau Teaching a Black-Box Learner
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman The Evolved Transformer
David So, Quoc Le, Chen Liang The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare The Wasserstein Transform
Facundo Memoli, Zane Smith, Zhengchao Wan Theoretically Principled Trade-Off Between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordan Towards a Deep and Unified Understanding of Deep Neural Models in NLP
Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie Towards a Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Daniel Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein Understanding and Correcting Pathologies in the Training of Learned Optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein Understanding the Impact of Entropy on Policy Optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller Universal Multi-Party Poisoning Attacks
Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed Unreproducible Research Is Reproducible
Xavier Bouthillier, César Laurent, Pascal Vincent Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, Kevin Mcguinness Variational Annealing of GANs: A Langevin Perspective
Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy Bobashev, Lawrence Carin Duke Variational Implicit Processes
Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato Variational Laplace Autoencoders
Yookoon Park, Chris Kim, Gunhee Kim When Samples Are Strategically Selected
Hanrui Zhang, Yu Cheng, Vincent Conitzer White-Box vs Black-Box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Herve Jegou Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty