NeurIPS 2020
1898 papers
3D Multi-Bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs, David Novotny, Sebastien Ehrhardt, Hanbyul Joo, Ben Graham, Andrea Vedaldi 3D Self-Supervised Methods for Medical Imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, Christoph Lippert 3D Shape Reconstruction from Vision and Touch
Edward Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk A Biologically Plausible Neural Network for Slow Feature Analysis
David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri B. Chklovskii A Boolean Task Algebra for Reinforcement Learning
Geraud Nangue Tasse, Steven James, Benjamin Rosman A Catalyst Framework for Minimax Optimization
Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri A Closer Look at the Training Strategy for Modern Meta-Learning
Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-lai Chung A Combinatorial Perspective on Transfer Learning
Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das A Dynamical Central Limit Theorem for Shallow Neural Networks
Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden A Game-Theoretic Analysis of Networked System Control for Common-Pool Resource Management Using Multi-Agent Reinforcement Learning
Arnu Pretorius, Scott Cameron, Elan van Biljon, Thomas Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan Shock, Alexandre Laterre, Karim Beguir A Kernel Test for Quasi-Independence
Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton A Mathematical Theory of Cooperative Communication
Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur, Chris Harris, Dale Schuurmans A Mean-Field Analysis of Two-Player Zero-Sum Games
Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant Rotskoff, Joan Bruna A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton A Novel Variational Form of the Schatten-$p$ Quasi-Norm
Paris Giampouras, Rene Vidal, Athanasios Rontogiannis, Benjamin Haeffele A Robust Functional EM Algorithm for Incomplete Panel Count Data
Alexander Moreno, Zhenke Wu, Jamie Roslyn Yap, Cho Lam, David Wetter, Inbal Nahum-Shani, Walter Dempsey, James M. Rehg A Self-Tuning Actor-Critic Algorithm
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado P van Hasselt, David Silver, Satinder P. Singh A Shooting Formulation of Deep Learning
François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer A Simple Language Model for Task-Oriented Dialogue
Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher A Simple Normative Network Approximates Local Non-Hebbian Learning in the Cortex
Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan Sengupta, Dmitri B. Chklovskii A Spectral Energy Distance for Parallel Speech Synthesis
Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Bhavya Kailkhura, Jayaraman Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer A Study on Encodings for Neural Architecture Search
Colin White, Willie Neiswanger, Sam Nolen, Yash Savani A Theoretical Framework for Target Propagation
Alexander Meulemans, Francesco Carzaniga, Johan Suykens, João Sacramento, Benjamin F. Grewe A Topological Filter for Learning with Label Noise
Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, Chao Chen A Unified View of Label Shift Estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary Lipton Acceleration with a Ball Optimization Oracle
Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian Active Structure Learning of Causal DAGs via Directed Clique Trees
Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek, Xenophon Papademetris, James Duncan Adapting Neural Architectures Between Domains
Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu Adapting to Misspecification in Contextual Bandits
Dylan J Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert Adaptive Discretization for Model-Based Reinforcement Learning
Sean Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Yu Adaptive Gradient Quantization for Data-Parallel SGD
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya Adaptive Probing Policies for Shortest Path Routing
Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala Adaptive Reduced Rank Regression
Qiong Wu, Felix MF Wong, Yanhua Li, Zhenming Liu, Varun Kanade Adaptive Sampling for Stochastic Risk-Averse Learning
Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause Adversarial Attacks on Deep Graph Matching
Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou Adversarial Attacks on Linear Contextual Bandits
Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta Adversarial Bandits with Corruptions: Regret Lower Bound and No-Regret Algorithm
Lin Yang, Mohammad Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong Adversarial Blocking Bandits
Nicholas Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh Adversarial Example Games
Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, Will Hamilton Adversarial Learning for Robust Deep Clustering
Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu Adversarial Robustness of Supervised Sparse Coding
Jeremias Sulam, Ramchandran Muthukumar, Raman Arora Adversarial Robustness via Robust Low Rank Representations
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan Adversarial Soft Advantage Fitting: Imitation Learning Without Policy Optimization
Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai Adversarial Sparse Transformer for Time Series Forecasting
Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer Agnostic Learning with Multiple Objectives
Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang AI Feynman 2.0: Pareto-Optimal Symbolic Regression Exploiting Graph Modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark Almost Surely Stable Deep Dynamics
Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni An Analysis of SVD for Deep Rotation Estimation
Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia An Efficient Framework for Clustered Federated Learning
Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone An Operator View of Policy Gradient Methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux Applications of Common Entropy for Causal Inference
Murat Kocaoglu, Sanjay Shakkottai, Alexandros G Dimakis, Constantine Caramanis, Sriram Vishwanath Approximate Cross-Validation for Structured Models
Soumya Ghosh, Will Stephenson, Tin D Nguyen, Sameer Deshpande, Tamara Broderick Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos Attribute Prototype Network for Zero-Shot Learning
Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata Auto Learning Attention
Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh Autoregressive Score Matching
Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing Auxiliary Task Reweighting for Minimum-Data Learning
Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu AvE: Assistance via Empowerment
Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan Avoiding Side Effects by Considering Future Tasks
Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg Avoiding Side Effects in Complex Environments
Alex Turner, Neale Ratzlaff, Prasad Tadepalli Axioms for Learning from Pairwise Comparisons
Ritesh Noothigattu, Dominik Peters, Ariel D Procaccia Bad Global Minima Exist and SGD Can Reach Them
Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas Balanced Meta-SoftMax for Long-Tailed Visual Recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li Bandit Linear Control
Asaf Cassel, Tomer Koren Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi BanditPAM: Almost Linear Time K-Medoids Clustering via Multi-Armed Bandits
Mo Tiwari, Martin J Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony Barking up the Right Tree: An Approach to Search over Molecule Synthesis DAGs
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, José Miguel Hernández-Lobato Baxter Permutation Process
Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda Bayesian Attention Modules
Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos J. Storkey Bayesian Optimization for Iterative Learning
Vu Nguyen, Sebastian Schulze, Michael Osborne Bayesian Optimization of Risk Measures
Sait Cakmak, Raul Astudillo Marban, Peter Frazier, Enlu Zhou Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, Francois-Xavier Briol Bayesian Pseudocoresets
Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell BayReL: Bayesian Relational Learning for Multi-Omics Data Integration
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Xiaoning Qian Belief Propagation Neural Networks
Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, Soheil Feizi BERT Loses Patience: Fast and Robust Inference with Early Exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, Furu Wei Better Set Representations for Relational Reasoning
Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser Nam Lim, Austin R Benson Bidirectional Convolutional Poisson Gamma Dynamical Systems
Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed Big Self-Supervised Models Are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton Biologically Inspired Mechanisms for Adversarial Robustness
Manish Reddy Vuyyuru, Andrzej Banburski, Nishka Pant, Tomaso Poggio Black-Box Optimization with Local Generative Surrogates
Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrei Ustyuzhanin, Atilim Gunes Baydin Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Remi Munos, Michal Valko Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh BOSS: Bayesian Optimization over String Spaces
Henry Moss, David Leslie, Daniel Beck, Javier González, Paul Rayson BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, Eytan Bakshy Boundary Thickness and Robustness in Learning Models
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E Gonzalez, Kannan Ramchandran, Michael W. Mahoney BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz, Tommaso Salvatori BRP-NAS: Prediction-Based NAS Using GCNs
Lukasz Dudziak, Thomas Chau, Mohamed Abdelfattah, Royson Lee, Hyeji Kim, Nicholas Lane Calibrating CNNs for Lifelong Learning
Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai Calibrating Deep Neural Networks Using Focal Loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania Can Graph Neural Networks Count Substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas Guibas Causal Discovery in Physical Systems from Videos
Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg Causal Estimation with Functional Confounders
Aahlad Puli, Adler J. Perotte, Rajesh Ranganath Causal Intervention for Weakly-Supervised Semantic Segmentation
Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun Certified Monotonic Neural Networks
Xingchao Liu, Xing Han, Na Zhang, Qiang Liu Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein Certifying Strategyproof Auction Networks
Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson Choice Bandits
Arpit Agarwal, Nicholas Johnson, Shivani Agarwal CO-Optimal Transport
Vayer Titouan, Ievgen Redko, Rémi Flamary, Nicolas Courty Co-Tuning for Transfer Learning
Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang Coded Sequential Matrix Multiplication for Straggler Mitigation
Nikhil Krishnan Muralee Krishnan, Seyederfan Hosseini, Ashish Khisti CogLTX: Applying BERT to Long Texts
Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, Dale Schuurmans ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano Collapsing Bandits and Their Application to Public Health Intervention
Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe Collegial Ensembles
Etai Littwin, Ben Myara, Sima Sabah, Joshua Susskind, Shuangfei Zhai, Oren Golan CoMIR: Contrastive Multimodal Image Representation for Registration
Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje Comparator-Adaptive Convex Bandits
Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová Compositional Generalization by Learning Analytical Expressions
Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang Conformal Symplectic and Relativistic Optimization
Guilherme Franca, Jeremias Sulam, Daniel Robinson, Rene Vidal Consequences of Misaligned AI
Simon Zhuang, Dylan Hadfield-Menell Consistent Plug-in Classifiers for Complex Objectives and Constraints
Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan Constrained Episodic Reinforcement Learning in Concave-Convex and Knapsack Settings
Kianté Brantley, Miro Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun Contextual Games: Multi-Agent Learning with Side Information
Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour Continual Deep Learning by Functional Regularisation of Memorable past
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan Continual Learning in Low-Rank Orthogonal Subspaces
Arslan Chaudhry, Naeemullah Khan, Puneet Dokania, Philip Torr Continuous Meta-Learning Without Tasks
James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone Continuous Regularized Wasserstein Barycenters
Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M Solomon Continuous Surface Embeddings
Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi ConvBERT: Improving BERT with Span-Based Dynamic Convolution
Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan Convolutional Generation of Textured 3D Meshes
Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang Coresets for Near-Convex Functions
Murad Tukan, Alaa Maalouf, Dan Feldman Coresets for Regressions with Panel Data
Lingxiao Huang, K Sudhir, Nisheeth Vishnoi Correlation Robust Influence Maximization
Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan Correspondence Learning via Linearly-Invariant Embedding
Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck Counterfactual Prediction for Bundle Treatment
Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He Counterfactual Predictions Under Runtime Confounding
Amanda Coston, Edward Kennedy, Alexandra Chouldechova Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Javen Qinfeng Shi, Anton van den Hengel Coupling-Based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas Cross-Validation Confidence Intervals for Test Error
Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester W. Mackey CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg CSER: Communication-Efficient SGD with Error Reset
Cong Xie, Shuai Zheng, Sanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin Curriculum by Smoothing
Samarth Sinha, Animesh Garg, Hugo Larochelle Cycle-Contrast for Self-Supervised Video Representation Learning
Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami Dark Experience for General Continual Learning: A Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara Debiased Contrastive Learning
Ching-Yao Chuang, Joshua W. Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka Debugging Tests for Model Explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim Decentralized Langevin Dynamics for Bayesian Learning
Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier Deep Active Inference Agents Using Monte-Carlo Methods
Zafeirios Fountas, Noor Sajid, Pedro Mediano, Karl Friston Deep Archimedean Copulas
Chun Kai Ling, Fei Fang, J. Zico Kolter Deep Automodulators
Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin Deep Direct Likelihood Knockoffs
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath Deep Evidential Regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus Deep Graph Pose: A Semi-Supervised Deep Graphical Model for Improved Animal Pose Tracking
Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, Rose Yu Deep Inverse Q-Learning with Constraints
Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker Deep Multimodal Fusion by Channel Exchanging
Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman, R Devon Hjelm Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou Deep Smoothing of the Implied Volatility Surface
Damien Ackerer, Natasa Tagasovska, Thibault Vatter Deep Statistical Solvers
Balthazar Donon, Zhengying Liu, Wenzhuo Liu, Isabelle Guyon, Antoine Marot, Marc Schoenauer Deep Subspace Clustering with Data Augmentation
Mahdi Abavisani, Alireza Naghizadeh, Dimitris Metaxas, Vishal Patel Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry Deep Transformers with Latent Depth
Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong Deeply Learned Spectral Total Variation Decomposition
Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola Schoenlieb Delay and Cooperation in Nonstochastic Linear Bandits
Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi Demystifying Orthogonal Monte Carlo and Beyond
Han Lin, Haoxian Chen, Krzysztof M Choromanski, Tianyi Zhang, Clement Laroche Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay N. Jain, Pieter Abbeel Depth Uncertainty in Neural Networks
Javier Antoran, James Allingham, José Miguel Hernández-Lobato Design Space for Graph Neural Networks
Jiaxuan You, Zhitao Ying, Jure Leskovec Detecting Interactions from Neural Networks via Topological Analysis
Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu Detection as Regression: Certified Object Detection with Median Smoothing
Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein Dialog Without Dialog Data: Learning Visual Dialog Agents from VQA Data
Michael Cogswell, Jiasen Lu, Rishabh Jain, Stefan Lee, Devi Parikh, Dhruv Batra Differentiable Causal Discovery from Interventional Data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin Differentiable Meta-Learning of Bandit Policies
Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer Differentiable Top-K with Optimal Transport
Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister Digraph Inception Convolutional Networks
Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, Andrew Lim Directional Pruning of Deep Neural Networks
Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng Dirichlet Graph Variational Autoencoder
Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang Discovering Conflicting Groups in Signed Networks
Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis Discovering Reinforcement Learning Algorithms
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Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho Discriminative Sounding Objects Localization via Self-Supervised Audiovisual Matching
Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou Disentangling by Subspace Diffusion
David Pfau, Irina Higgins, Alex Botev, Sébastien Racanière Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander Dissecting Neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama Distribution Matching for Crowd Counting
Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen Distributionally Robust Federated Averaging
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Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour Domain Generalization via Entropy Regularization
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Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama Dual-Resolution Correspondence Networks
Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
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Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou Early-Learning Regularization Prevents Memorization of Noisy Labels
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Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, Stephen J. Roberts Efficient Algorithms for Device Placement of DNN Graph Operators
Jakub M Tarnawski, Amar Phanishayee, Nikhil Devanur, Divya Mahajan, Fanny Nina Paravecino Efficient Contextual Bandits with Continuous Actions
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins Efficient Estimation of Neural Tuning During Naturalistic Behavior
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Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart J. Russell, Andrew Critch, Sergey Levine Empirical Likelihood for Contextual Bandits
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Weitang Liu, Xiaoyun Wang, John Owens, Yixuan Li Ensembling Geophysical Models with Bayesian Neural Networks
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Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, Junier B Oliva Exemplar Guided Active Learning
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Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C Holmes Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
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Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang Fair Performance Metric Elicitation
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Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen Graph Cross Networks with Vertex Infomax Pooling
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Fabian Mentzer, George D Toderici, Michael Tschannen, Eirikur Agustsson High-Throughput Synchronous Deep RL
Iou-Jen Liu, Raymond Yeh, Alexander Schwing Higher-Order Certification for Randomized Smoothing
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Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal HRN: A Holistic Approach to One Class Learning
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