ICML 2021
1182 papers
1-Bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed
Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He 12-Lead ECG Reconstruction via Koopman Operators
Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha A Discriminative Technique for Multiple-Source Adaptation
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang A Distribution-Dependent Analysis of Meta Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvari A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, Yichuan Zhang A Language for Counterfactual Generative Models
Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama A Large-Scale Benchmark for Few-Shot Program Induction and Synthesis
Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Joshua Tenenbaum A New Formalism, Method and Open Issues for Zero-Shot Coordination
Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob Foerster A New Representation of Successor Features for Transfer Across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen Karimpanal George, Sunil Gupta, Santu Rana, Svetha Venkatesh A Novel Method to Solve Neural Knapsack Problems
Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, Noseong Park A Novel Sequential Coreset Method for Gradient Descent Algorithms
Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos Freris, Hu Ding A Nullspace Property for Subspace-Preserving Recovery
Mustafa D Kaba, Chong You, Daniel P Robinson, Enrique Mallada, Rene Vidal A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Dong Ki Kim, Miao Liu, Matthew D Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan How A Receptor Skeleton for Capsule Neural Networks
Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z Chen, Jian Wu A Statistical Perspective on Distillation
Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Seungyeon Kim, Sanjiv Kumar A Tale of Two Efficient and Informative Negative Sampling Distributions
Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang A Wasserstein Minimax Framework for Mixed Linear Regression
Theo Diamandis, Yonina Eldar, Alireza Fallah, Farzan Farnia, Asuman Ozdaglar Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu Accelerating Gossip SGD with Periodic Global Averaging
Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt Accurate Post Training Quantization with Small Calibration Sets
Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry ACE: Explaining Cluster from an Adversarial Perspective
Yang Young Lu, Timothy C Yu, Giancarlo Bonora, William Stafford Noble Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan C Julian, Chelsea Finn, Sergey Levine Active Covering
Heinrich Jiang, Afshin Rostamizadeh Active Deep Probabilistic Subsampling
Hans Van Gorp, Iris Huijben, Bastiaan S Veeling, Nicola Pezzotti, Ruud J. G. Van Sloun Active Slices for Sliced Stein Discrepancy
Wenbo Gong, Kaibo Zhang, Yingzhen Li, Jose Miguel Hernandez-Lobato Active Testing: Sample-Efficient Model Evaluation
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael Mahoney, Joseph Gonzalez AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo, Peilin Zhao, Peng Wang, Mingkui Tan Additive Error Guarantees for Weighted Low Rank Approximation
Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Alexander V Rogozin, Alexander Gasnikov Adversarial Dueling Bandits
Aadirupa Saha, Tomer Koren, Yishay Mansour Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei Li Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur AGENT: A Benchmark for Core Psychological Reasoning
Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua Tenenbaum, Tomer Ullman Aggregating from Multiple Target-Shifted Sources
Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles X Ling, Boyu Wang Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits
Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu Alternative Microfoundations for Strategic Classification
Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang Annealed Flow Transport Monte Carlo
Michael Arbel, Alex Matthews, Arnaud Doucet Approximate Group Fairness for Clustering
Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris Holmes, Mert Gurbuzbalaban, Umut Simsekli Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji Asymptotics of Ridge Regression in Convolutional Models
Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K Fletcher Asynchronous Decentralized Optimization with Implicit Stochastic Variance Reduction
Kenta Niwa, Guoqiang Zhang, W. Bastiaan Kleijn, Noboru Harada, Hiroshi Sawada, Akinori Fujino Automatic Variational Inference with Cascading Flows
Luca Ambrogioni, Gianluigi Silvestri, Marcel Gerven AutoSampling: Search for Effective Data Sampling Schedules
Ming Sun, Haoxuan Dou, Baopu Li, Junjie Yan, Wanli Ouyang, Lei Cui Backdoor Scanning for Deep Neural Networks Through K-Arm Optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, Xiangyu Zhang BANG: Bridging Autoregressive and Non-Autoregressive Generation with Large Scale Pretraining
Weizhen Qi, Yeyun Gong, Jian Jiao, Yu Yan, Weizhu Chen, Dayiheng Liu, Kewen Tang, Houqiang Li, Jiusheng Chen, Ruofei Zhang, Ming Zhou, Nan Duan BASE Layers: Simplifying Training of Large, Sparse Models
Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer Bayesian Attention Belief Networks
Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, Jose Miguel Hernandez-Lobato Bayesian Optimization over Hybrid Spaces
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis Best Arm Identification in Graphical Bilinear Bandits
Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre Better Training Using Weight-Constrained Stochastic Dynamics
Benedict Leimkuhler, Tiffany J Vlaar, Timothée Pouchon, Amos Storkey Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P Cunningham Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang Blind Pareto Fairness and Subgroup Robustness
Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, Mengdi Wang BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos Breaking the Deadly Triad with a Target Network
Shangtong Zhang, Hengshuai Yao, Shimon Whiteson Breaking the Limits of Message Passing Graph Neural Networks
Muhammet Balcilar, Pierre Heroux, Benoit Gauzere, Pascal Vasseur, Sebastien Adam, Paul Honeine CARTL: Cooperative Adversarially-Robust Transfer Learning
Dian Chen, Hongxin Hu, Qian Wang, Li Yinli, Cong Wang, Chao Shen, Qi Li Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo B Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J Geras Catformer: Designing Stable Transformers via Sensitivity Analysis
Jared Q Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu Classification with Rejection Based on Cost-Sensitive Classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama Collaborative Bayesian Optimization with Fair Regret
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet Combinatorial Blocking Bandits with Stochastic Delays
Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai Compositional Video Synthesis with Action Graphs
Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi Conjugate Energy-Based Models
Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem Van De Meent Consensus Control for Decentralized Deep Learning
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian Stich Context-Aware Online Collective Inference for Templated Graphical Models
Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor Continuous Coordination as a Realistic Scenario for Lifelong Learning
Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar Continuous-Time Model-Based Reinforcement Learning
Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki Contrastive Learning Inverts the Data Generating Process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel Convex Regularization in Monte-Carlo Tree Search
Tuan Q Dam, Carlo D’Eramo, Jan Peters, Joni Pajarinen ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane D’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, Levent Sagun Correcting Exposure Bias for Link Recommendation
Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos Cross-Domain Imitation from Observations
Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit K. Roy-Chowdhury Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar Cumulants of Hawkes Processes Are Robust to Observation Noise
William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximillian Nickel, Ari Morcos, Brenden M Lake DANCE: Enhancing Saliency Maps Using Decoys
Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin Data Augmentation for Meta-Learning
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein Data-Efficient Hindsight Off-Policy Option Learning
Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller Debiasing a First-Order Heuristic for Approximate Bi-Level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Q Davis, Adrian Weller Debiasing Model Updates for Improving Personalized Federated Training
Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas, Matthew Mattina, Paul Whatmough, Venkatesh Saligrama Decomposable Submodular Function Minimization via Maximum Flow
Kyriakos Axiotis, Adam Karczmarz, Anish Mukherjee, Piotr Sankowski, Adrian Vladu Decomposed Mutual Information Estimation for Contrastive Representation Learning
Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoff Gordon, Philip Bachman, Remi Tachet Des Combes Deep Continuous Networks
Nergis Tomen, Silvia-Laura Pintea, Jan Van Gemert Deep Generative Learning via Schrödinger Bridge
Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang Deep Kernel Processes
Laurence Aitchison, Adam Yang, Sebastian W. Ober Deep Latent Graph Matching
Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li Deeply-Debiased Off-Policy Interval Estimation
Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen DeepWalking Backwards: From Embeddings Back to Graphs
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis Delving into Deep Imbalanced Regression
Yuzhe Yang, Kaiwen Zha, Yingcong Chen, Hao Wang, Dina Katabi Differentiable Spatial Planning Using Transformers
Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia Diffusion Earth Mover’s Distance and Distribution Embeddings
Alexander Y Tong, Guillaume Huguet, Amine Natik, Kincaid Macdonald, Manik Kuchroo, Ronald Coifman, Guy Wolf, Smita Krishnaswamy Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej L Wiatrak, Angus Brayne, Paidi Creed, Saee Paliwal Directional Bias Amplification
Angelina Wang, Olga Russakovsky Directional Graph Networks
Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, Pietro Lió Discovering Symbolic Policies with Deep Reinforcement Learning
Mikel Landajuela, Brenden K Petersen, Sookyung Kim, Claudio P Santiago, Ruben Glatt, Nathan Mundhenk, Jacob F Pettit, Daniel Faissol Discretization Drift in Two-Player Games
Mihaela C Rosca, Yan Wu, Benoit Dherin, David Barrett Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Ankit Singh Rawat, Aditya K Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix Yu, Sashank Reddi, Sanjiv Kumar Dissecting Supervised Contrastive Learning
Florian Graf, Christoph Hofer, Marc Niethammer, Roland Kwitt DORO: Distributional and Outlier Robust Optimization
Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu DriftSurf: Stable-State / Reactive-State Learning Under Concept Drift
Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura, Phillip B Gibbons Dropout: Explicit Forms and Capacity Control
Raman Arora, Peter Bartlett, Poorya Mianjy, Nathan Srebro Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour Dynamic Balancing for Model Selection in Bandits and RL
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit Dynamic Game Theoretic Neural Optimizer
Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou E(n) Equivariant Graph Neural Networks
Vı́ctor Garcia Satorras, Emiel Hoogeboom, Max Welling Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Dustin Morrill, Ryan D’Orazio, Marc Lanctot, James R Wright, Michael Bowling, Amy R Greenwald Efficient Generative Modelling of Protein Structure Fragments Using a Deep Markov Model
Christian B Thygesen, Christian Skjødt Steenmans, Ahmad Salim Al-Sibahi, Lys Sanz Moreta, Anders Bundgård Sørensen, Thomas Hamelryck Efficient Lottery Ticket Finding: Less Data Is More
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang Efficient Online Learning for Dynamic K-Clustering
Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture
Chenfeng Miao, Liang Shuang, Zhengchen Liu, Chen Minchuan, Jun Ma, Shaojun Wang, Jing Xiao EL-Attention: Memory Efficient Lossless Attention for Generation
Yu Yan, Jiusheng Chen, Weizhen Qi, Nikhil Bhendawade, Yeyun Gong, Nan Duan, Ruofei Zhang Elastic Graph Neural Networks
Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado Van Hasselt End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski Ensemble Bootstrapping for Q-Learning
Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir Environment Inference for Invariant Learning
Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong Explanations for Monotonic Classifiers.
Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper, Alexey Ignatiev, Nina Narodytska Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning Against Adversarial Attacks
Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, Da Yan F-Domain Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler Fair Selective Classification via Sufficiency
Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W Wornell Fairness and Bias in Online Selection
Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims Fast Active Learning for Pure Exploration in Reinforcement Learning
Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko Fast Projection onto Convex Smooth Constraints
Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Levy Faster Kernel Matrix Algebra via Density Estimation
Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner Federated Composite Optimization
Honglin Yuan, Manzil Zaheer, Sashank Reddi Federated Continual Learning with Weighted Inter-Client Transfer
Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, Sung Ju Hwang Federated Learning of User Verification Models Without Sharing Embeddings
Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling Few-Shot Conformal Prediction with Auxiliary Tasks
Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay Few-Shot Neural Architecture Search
Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo Finding K in Latent $k-$ Polytope
Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar Finding Relevant Information via a Discrete Fourier Expansion
Mohsen Heidari, Jithin Sreedharan, Gil I Shamir, Wojciech Szpankowski Follow-the-Regularized-Leader Routes to Chaos in Routing Games
Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz, Georgios Piliouras From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls Function Contrastive Learning of Transferable Meta-Representations
Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf Fundamental Tradeoffs in Distributionally Adversarial Training
Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai Gaussian Process-Based Real-Time Learning for Safety Critical Applications
Armin Lederer, Alejandro J Ordóñez Conejo, Korbinian A Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche Generalizable Episodic Memory for Deep Reinforcement Learning
Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang Generalization Error Bound for Hyperbolic Ordinal Embedding
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza Generating Images with Sparse Representations
Charlie Nash, Jacob Menick, Sander Dieleman, Peter Battaglia Geometric Convergence of Elliptical Slice Sampling
Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clement Hongler, Wulfram Gerstner, Johanni Brea Global Prosody Style Transfer Without Text Transcriptions
Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David Cox, Mark Hasegawa-Johnson Globally-Robust Neural Networks
Klas Leino, Zifan Wang, Matt Fredrikson Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov GRAND: Graph Neural Diffusion
Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, Emanuele Rossi Graph Contrastive Learning Automated
Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang Graph Mixture Density Networks
Federico Errica, Davide Bacciu, Alessio Micheli Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf Grey-Box Extraction of Natural Language Models
Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd, Boris Köpf Grid-Functioned Neural Networks
Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth Group Fisher Pruning for Practical Network Compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue, Xinjiang Wang, Yimin Chen, Wenming Yang, Qingmin Liao, Wayne Zhang HAWQ-V3: Dyadic Neural Network Quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, Kurt Keutzer Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile Hierarchical VAEs Know What They Don’t Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe High Confidence Generalization for Reinforcement Learning
James Kostas, Yash Chandak, Scott M Jordan, Georgios Theocharous, Philip Thomas Homomorphic Sensing: Sparsity and Noise
Liangzu Peng, Boshi Wang, Manolis Tsakiris How Could Neural Networks Understand Programs?
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu How Do Adam and Training Strategies Help BNNs Optimization
Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng How Framelets Enhance Graph Neural Networks
Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montufar How Important Is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, Caiming Xiong Hyperparameter Selection for Imitation Learning
Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphael Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin I-BERT: Integer-Only BERT Quantization
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Ehsan M Abbasnejad, Reza Haffari Image-Level or Object-Level? a Tale of Two Resampling Strategies for Long-Tailed Detection
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Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne Implicit Bias of Linear RNNs
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Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhiming Ma Improving Generalization in Meta-Learning via Task Augmentation
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Yangjun Ruan, Karen Ullrich, Daniel S Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison Improving Ultrametrics Embeddings Through Coresets
Vincent Cohen-Addad, Rémi De Joannis De Verclos, Guillaume Lagarde In-Database Regression in Input Sparsity Time
Rajesh Jayaram, Alireza Samadian, David Woodruff, Peng Ye Incentivizing Compliance with Algorithmic Instruments
Dung Daniel T Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu Information Obfuscation of Graph Neural Networks
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Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu, Yang Zhou, Yelong Shen, Tianshi Che, Ruoming Jin, Dejing Dou Interaction-Grounded Learning
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Khanh X Nguyen, Dipendra Misra, Robert Schapire, Miroslav Dudik, Patrick Shafto Inverse Constrained Reinforcement Learning
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Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn K-Shot NAS: Learnable Weight-Sharing for NAS with K-Shot Supernets
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Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, Wei Chen Kernel Continual Learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees Snoek Kernel Stein Discrepancy Descent
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Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, Hongxia Yang Label Inference Attacks from Log-Loss Scores
Abhinav Aggarwal, Shiva Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot LAMDA: Label Matching Deep Domain Adaptation
Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung Large-Scale Multi-Agent Deep FBSDEs
Tianrong Chen, Ziyi O Wang, Ioannis Exarchos, Evangelos Theodorou LARNet: Lie Algebra Residual Network for Face Recognition
Xiaolong Yang, Xiaohong Jia, Dihong Gong, Dong-Ming Yan, Zhifeng Li, Wei Liu Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Samuel S Schoenholz, Ekin D Cubuk Learning a Universal Template for Few-Shot Dataset Generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver Learning Bounds for Open-Set Learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang Learning Curves for Analysis of Deep Networks
Derek Hoiem, Tanmay Gupta, Zhizhong Li, Michal Shlapentokh-Rothman Learning Diverse-Structured Networks for Adversarial Robustness
Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama Learning from Biased Data: A Semi-Parametric Approach
Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry Learning from Similarity-Confidence Data
Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama Learning Generalized Intersection over Union for Dense Pixelwise Prediction
Jiaqian Yu, Jingtao Xu, Yiwei Chen, Weiming Li, Qiang Wang, Byungin Yoo, Jae-Joon Han Learning in Nonzero-Sum Stochastic Games with Potentials
David H Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang Learning Intra-Batch Connections for Deep Metric Learning
Jenny Denise Seidenschwarz, Ismail Elezi, Laura Leal-Taixé Learning Neural Network Subspaces
Mitchell Wortsman, Maxwell C Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari Learning Online Algorithms with Distributional Advice
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Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah Learning to Rehearse in Long Sequence Memorization
Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao Learning Transferable Visual Models from Natural Language Supervision
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Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou Leveraged Weighted Loss for Partial Label Learning
Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin Leveraging Good Representations in Linear Contextual Bandits
Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta Leveraging Non-Uniformity in First-Order Non-Convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans Leveraging Public Data for Practical Private Query Release
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Michael J Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim Light RUMs
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Yuhuai Wu, Markus N Rabe, Wenda Li, Jimmy Ba, Roger B Grosse, Christian Szegedy Local Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev Locally Private K-Means in One Round
Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Gregory Benton, Wesley Maddox, Sanae Lotfi, Andrew Gordon Gordon Wilson Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, Yanzhi Wang Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Pashootan Vaezipoor, Andrew C Li, Rodrigo A Toro Icarte, Sheila A. Mcilraith Making Paper Reviewing Robust to Bid Manipulation Attacks
Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens Van Der Maaten, Kilian Weinberger Mandoline: Model Evaluation Under Distribution Shift
Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Re Marginal Contribution Feature Importance - An Axiomatic Approach for Explaining Data
Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Libi Weiss Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach MARINA: Faster Non-Convex Distributed Learning with Compression
Eduard Gorbunov, Konstantin P. Burlachenko, Zhize Li, Peter Richtarik Markpainting: Adversarial Machine Learning Meets Inpainting
David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
Kuruge Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Rahimi Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav Matrix Completion with Model-Free Weighting
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Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama MC-LSTM: Mass-Conserving LSTM
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Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera Y Arcas Meta-Thompson Sampling
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Nicolas Perez-Nieves, Yaodong Yang, Oliver Slumbers, David H Mguni, Ying Wen, Jun Wang Momentum Residual Neural Networks
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré Monotonic Robust Policy Optimization with Model Discrepancy
Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong Monte Carlo Variational Auto-Encoders
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Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu MSA Transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives Muesli: Combining Improvements in Policy Optimization
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado Van Hasselt Multi-Receiver Online Bayesian Persuasion
Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti Multidimensional Scaling: Approximation and Complexity
Erik Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, Sergey Levine Navigation Turing Test (NTT): Learning to Evaluate Human-like Navigation
Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam R Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokra, Danilo Jimenez Rezende Neural Architecture Search Without Training
Joe Mellor, Jack Turner, Amos Storkey, Elliot J Crowley Neural Feature Matching in Implicit 3D Representations
Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves Neural Pharmacodynamic State Space Modeling
Zeshan M Hussain, Rahul G. Krishnan, David Sontag Neural Rough Differential Equations for Long Time Series
James Morrill, Cristopher Salvi, Patrick Kidger, James Foster Neural SDEs as Infinite-Dimensional GANs
Patrick Kidger, James Foster, Xuechen Li, Terry J Lyons Neural Symbolic Regression That Scales
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo Newton Method over Networks Is Fast up to the Statistical Precision
Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky, Alexander Gasnikov Nonmyopic Multifidelity Acitve Search
Quan Nguyen, Arghavan Modiri, Roman Garnett Not All Memories Are Created Equal: Learning to Forget by Expiring
Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan Oblivious Sketching for Logistic Regression
Alexander Munteanu, Simon Omlor, David Woodruff Off-Belief Learning
Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, Jakob Foerster Off-Policy Confidence Sequences
Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna Offline Meta-Reinforcement Learning with Advantage Weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn Offline Reinforcement Learning with Pseudometric Learning
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Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip M Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler On a Combination of Alternating Minimization and Nesterov’s Momentum
Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa, Alexander Gasnikov On Disentangled Representations Learned from Correlated Data
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Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna On Monotonic Linear Interpolation of Neural Network Parameters
James R Lucas, Juhan Bae, Michael R Zhang, Stanislav Fort, Richard Zemel, Roger B Grosse On Proximal Policy Optimization’s Heavy-Tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, Zico Kolter, Zachary Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar On Robust Mean Estimation Under Coordinate-Level Corruption
Zifan Liu, Jong Ho Park, Theodoros Rekatsinas, Christos Tzamos On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvari, Dale Schuurmans On the Predictability of Pruning Across Scales
Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit One Pass Late Fusion Multi-View Clustering
Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, En Zhu Online A-Optimal Design and Active Linear Regression
Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet Online Graph Dictionary Learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra Online Learning with Optimism and Delay
Genevieve E Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey Online Selection Problems Against Constrained Adversary
Zhihao Jiang, Pinyan Lu, Zhihao Gavin Tang, Yuhao Zhang Oops I Took a Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain Optimization Planning for 3D ConvNets
Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei Optimizing Black-Box Metrics with Iterative Example Weighting
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Sanmi Koyejo Optimizing Persistent Homology Based Functions
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David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville Outlier-Robust Optimal Transport
Debarghya Mukherjee, Aritra Guha, Justin M Solomon, Yuekai Sun, Mikhail Yurochkin PAC-Learning for Strategic Classification
Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause Parallel Droplet Control in MEDA Biochips Using Multi-Agent Reinforcement Learning
Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty, Cy Lee Parallel Tempering on Optimized Paths
Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Cote Parameter-Free Locally Accelerated Conditional Gradients
Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta Parametric Graph for Unimodal Ranking Bandit
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont, Boammani Aser Lompo Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy Cohen, Zinan Lin, Kevin Chan, Charles Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar Path Planning Using Neural A* Search
Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki Perceiver: General Perception with Iterative Attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira Permutation Weighting
David Arbour, Drew Dimmery, Arjun Sondhi Personalized Federated Learning Using Hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik Phasic Policy Gradient
Karl W Cobbe, Jacob Hilton, Oleg Klimov, John Schulman PID Accelerated Value Iteration Algorithm
Amir-Massoud Farahmand, Mohammad Ghavamzadeh PODS: Policy Optimization via Differentiable Simulation
Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama Policy Gradient Bayesian Robust Optimization for Imitation Learning
Zaynah Javed, Daniel S Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca Dragan, Ken Goldberg Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu Poolingformer: Long Document Modeling with Pooling Attention
Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, Weizhu Chen Post-Selection Inference with HSIC-Lasso
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Minqi Jiang, Edward Grefenstette, Tim Rocktäschel Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
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Shariq Iqbal, Christian A Schroeder De Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton Reasoning over Virtual Knowledge Bases with Open Predicate Relations
Haitian Sun, Patrick Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W Cohen Regularized Submodular Maximization at Scale
Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi Reinforcement Learning for Cost-Aware Markov Decision Processes
Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David Kraemer Reinforcement Learning with Prototypical Representations
Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto Relative Deviation Margin Bounds
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh Relative Positional Encoding for Transformers with Linear Complexity
Antoine Liutkus, Ondřej Cı́fka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gael Richard REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning
Tadashi Kozuno, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel Reward Identification in Inverse Reinforcement Learning
Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon Riemannian Convex Potential Maps
Samuel Cohen, Brandon Amos, Yaron Lipman RNNRepair: Automatic RNN Repair via Model-Based Analysis
Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing Robust Asymmetric Learning in POMDPs
Andrew Warrington, Jonathan W Lavington, Adam Scibior, Mark Schmidt, Frank Wood Robust Learning-Augmented Caching: An Experimental Study
Jakub Chłędowski, Adam Polak, Bartosz Szabucki, Konrad Tomasz Żołna Robust Representation Learning via Perceptual Similarity Metrics
Saeid A Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal Robust Unsupervised Learning via L-Statistic Minimization
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Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin Self-Tuning for Data-Efficient Deep Learning
Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang Selfish Sparse RNN Training
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Min-Hwan Oh, Garud Iyengar, Assaf Zeevi Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela C Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu SpreadsheetCoder: Formula Prediction from Semi-Structured Context
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Linfeng Liu, Michael C Hughes, Soha Hassoun, Liping Liu Strategic Classification in the Dark
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Andrew J Wagenmaker, Max Simchowitz, Kevin Jamieson Taylor Expansion of Discount Factors
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