ICML 2020
1075 papers
“Other-Play” for Zero-Shot Coordination
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster (Locally) Differentially Private Combinatorial Semi-Bandits
Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang A Distributional View on Multi-Objective Policy Optimization
Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang A New Regret Analysis for Adam-Type Algorithms
Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher A Pairwise Fair and Community-Preserving Approach to K-Center Clustering
Brian Brubach, Darshan Chakrabarti, John Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas A Swiss Army Knife for Minimax Optimal Transport
Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Lirong Dai A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian Stich Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar Active World Model Learning with Progress Curiosity
Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard Fair Adaptive Region-Based Active Learning
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Christos Matsoukas, Albert Bou Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith Adversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, Yejin Choi Adversarial Mutual Information for Text Generation
Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy Almost Tune-Free Variance Reduction
Bingcong Li, Lingda Wang, Georgios B. Giannakis Amortised Learning by Wake-Sleep
Li Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem Van De Meent An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo An Imitation Learning Approach for Cache Replacement
Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang Asynchronous Coagent Networks
James Kostas, Chris Nota, Philip Thomas Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli Attentive Group Equivariant Convolutional Networks
David Romero, Erik Bekkers, Jakub Tomczak, Mark Hoogendoorn AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang Automated Synthetic-to-Real Generalization
Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren, Moritz Hardt, Joshua Blumenstock Bandits for BMO Functions
Tianyu Wang, Cynthia Rudin Bandits with Adversarial Scaling
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme Batch Reinforcement Learning with Hyperparameter Gradients
Byungjun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, Stephen Roberts Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya Ryali, John Hopfield, Leopold Grinberg, Dmitry Krotov Black-Box Methods for Restoring Monotonicity
Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos Boosted Histogram Transform for Regression
Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin Boosting Deep Neural Network Efficiency with Dual-Module Inference
Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Remi Munos, Mohammad Gheshlaghi Azar Born-Again Tree Ensembles
Thibaut Vidal, Maximilian Schiffer BoXHED: Boosted eXact Hazard Estimator with Dynamic Covariates
Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao, Donald Lee Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan Budgeted Online Influence Maximization
Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman, Xinyi Chen, Sham Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang Can Autonomous Vehicles Identify, Recover from, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigkas, Rowan Mcallister, Nicholas Rhinehart, Sergey Levine, Yarin Gal Causal Modeling for Fairness in Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel Causal Strategic Linear Regression
Yonadav Shavit, Benjamin Edelman, Brian Axelrod Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten Channel Equilibrium Networks for Learning Deep Representation
Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo Closing the Convergence Gap of SGD Without Replacement
Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos CLUB: A Contrastive Log-Ratio Upper Bound of Mutual Information
Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang Concise Explanations of Neural Networks Using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier Constant Curvature Graph Convolutional Networks
Gregor Bachmann, Gary Becigneul, Octavian Ganea Context Aware Local Differential Privacy
Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun Continuous Graph Neural Networks
Louis-Pascal Xhonneux, Meng Qu, Jian Tang Continuous Time Bayesian Networks with Clocks
Nicolai Engelmann, Dominik Linzner, Heinz Koeppl Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli ControlVAE: Controllable Variational Autoencoder
Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev Cost-Effective Interactive Attention Learning with Neural Attention Processes
Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin Debiased Sinkhorn Barycenters
Hicham Janati, Marco Cuturi, Alexandre Gramfort Decoupled Greedy Learning of CNNs
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon Deep Coordination Graphs
Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson Deep Divergence Learning
Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis Deep Isometric Learning for Visual Recognition
Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta Deep PQR: Solving Inverse Reinforcement Learning Using Anchor Actions
Sinong Geng, Houssam Nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar Deep Reinforcement Learning with Robust and Smooth Policy
Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao Deep Streaming Label Learning
Zhen Wang, Liu Liu, Dacheng Tao DeepCoDA: Personalized Interpretability for Compositional Health Data
Thomas Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh Defense Through Diverse Directions
Christopher Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin Differentiating Through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa Discriminative Adversarial Search for Abstractive Summarization
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano Distance Metric Learning with Joint Representation Diversification
Xu Chu, Yang Lin, Yasha Wang, Xiting Wang, Hailong Yu, Xin Gao, Qi Tong Distribution Augmentation for Generative Modeling
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever Do RNN and LSTM Have Long Memory?
Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama Does Label Smoothing Mitigate Label Noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar Domain Adaptive Imitation Learning
Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon Double-Loop Unadjusted Langevin Algorithm
Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher Doubly Robust Off-Policy Evaluation with Shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudik DROCC: Deep Robust One-Class Classification
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro Efficiently Sampling Functions from Gaussian Process Posteriors
James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, Sueyeon Chung Encoding Musical Style with Transformer Autoencoders
Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel Energy-Based Processes for Exchangeable Data
Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans Entropy Minimization in Emergent Languages
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni Equivariant Neural Rendering
Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Josh Susskind, Qi Shan Error Estimation for Sketched SVD via the Bootstrap
Miles Lopes, N. Benjamin Erichson, Michael Mahoney Error-Bounded Correction of Noisy Labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients
Ashley Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt Evaluating the Performance of Reinforcement Learning Algorithms
Scott Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip Thomas Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer Explainable K-Means and K-Medians Clustering
Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost Explaining Groups of Points in Low-Dimensional Representations
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar Explicit Gradient Learning for Black-Box Optimization
Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-I-Nieto, Jordi Torres Extra-Gradient with Player Sampling for Faster Convergence in N-Player Games
Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna Extreme Multi-Label Classification from Aggregated Labels
Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon Fair Generative Modeling via Weak Supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon Fair K-Centers via Maximum Matching
Matthew Jones, Huy Nguyen, Thy Nguyen Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob Ohannessian, Nathan Srebro Fairwashing Explanations with Off-Manifold Detergent
Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel Fast and Three-Rious: Speeding up Weak Supervision with Triplet Methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, Christopher Re Fast Computation of Nash Equilibria in Imperfect Information Games
Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga Faster Graph Embeddings via Coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang Feature Quantization Improves GAN Training
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen Feature Selection Using Stochastic Gates
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger Federated Learning with Only Positive Labels
Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins Forecasting Sequential Data Using Consistent Koopman Autoencoders
Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, Shira Kritchman From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtarik Frustratingly Simple Few-Shot Object Detection
Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jeremy Rapin, Morgane Riviere, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier Fundamental Tradeoffs Between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen Gamification of Pure Exploration for Linear Bandits
Rémy Degenne, Pierre Menard, Xuedong Shang, Michal Valko Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson Fletcher Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer Generating Programmatic Referring Expressions via Program Synthesis
Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik Generative Pretraining from Pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White Graph Filtration Learning
Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt Graph Homomorphism Convolution
Hoang Nguyen, Takanori Maehara Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu Graph Structure of Neural Networks
Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie Growing Action Spaces
Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve Growing Adaptive Multi-Hyperplane Machines
Nemanja Djuric, Zhuang Wang, Slobodan Vucetic Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar Healing Products of Gaussian Process Experts
Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth High-Dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi How Good Is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin How to Solve Fair K-Center in Massive Data Models
Ashish Chiplunkar, Sagar Kale, Sivaramakrishnan Natarajan Ramamoorthy Hypernetwork Approach to Generating Point Clouds
Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski Identifying Statistical Bias in Dataset Replication
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry Implicit Competitive Regularization in GANs
Florian Schaefer, Hongkai Zheng, Animashree Anandkumar Implicit Differentiation of Lasso-Type Models for Hyperparameter Optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman Implicit Regularization of Random Feature Models
Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel Improved Optimistic Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Clement Calauzenes, Olivier Fercoq Improving Generative Imagination in Object-Centric World Models
Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson, Dr.Regina Barzilay, Tommi Jaakkola Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu, Caglar Gulcehre, Thomas Paine, Matt Hoffman, Razvan Pascanu Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, Dan Alistarh Infinite Attention: NNGP and NTK for Deep Attention Networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak Inter-Domain Deep Gaussian Processes
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, Finale Doshi-Velez Invariant Causal Prediction for Block MDPs
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola Invariant Risk Minimization Games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar Involutive MCMC: A Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov Is Local SGD Better than Minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro K-Means++: Few More Steps Yield Constant Approximation
Davin Choo, Christoph Grunau, Julian Portmann, Vaclav Rozhon Label-Noise Robust Domain Adaptation
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao Laplacian Regularized Few-Shot Learning
Imtiaz Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed Latent Bernoulli Autoencoder
Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino Latent Variable Modelling with Hyperbolic Normalizing Flows
Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, Will Hamilton Learning Algebraic Multigrid Using Graph Neural Networks
Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi Learning and Sampling of Atomic Interventions from Observations
Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam Learning Autoencoders with Relational Regularization
Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin Learning Calibratable Policies Using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht Learning De-Biased Representations with Biased Representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland Learning Disconnected Manifolds: A No GAN’s Land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary Learning Flat Latent Manifolds with VAEs
Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick Van Der Smagt Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy, Anca Dragan, Sergey Levine, Shane Legg, Jan Leike Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis Learning near Optimal Policies with Low Inherent Bellman Error
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