Book
Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)
Edited by:
H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin
A graph similarity for deep learning Seongmin Ok An Unsupervised Information-Theoretic Perceptual Quality Metric Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen Self-Supervised MultiModal Versatile Networks Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method Simiao Ren, Willie Padilla, Jordan Malof Off-Policy Evaluation and Learning for External Validity under a Covariate Shift Masatoshi Uehara, Masahiro Kato, Shota Yasui Neural Methods for Point-wise Dependency Estimation Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Russ R. Salakhutdinov Fast and Flexible Temporal Point Processes with Triangular Maps Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann Backpropagating Linearly Improves Transferability of Adversarial Examples Yiwen Guo, Qizhang Li, Hao Chen PyGlove: Symbolic Programming for Automated Machine Learning Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamas Erdelyi, Cameron Musco, Christopher Musco Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds Nicholas Harvey, Christopher Liaw, Tasuku Soma Synbols: Probing Learning Algorithms with Synthetic Datasets Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matthew Craddock, Laurent Charlin, David Vázquez Adversarially Robust Streaming Algorithms via Differential Privacy Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong Cascaded Text Generation with Markov Transformers Yuntian Deng, Alexander Rush Improving Local Identifiability in Probabilistic Box Embeddings Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna, Daniel R. Sheldon Deep reconstruction of strange attractors from time series William Gilpin Reciprocal Adversarial Learning via Characteristic Functions Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic Statistical Guarantees of Distributed Nearest Neighbor Classification Jiexin Duan, Xingye Qiao, Guang Cheng Stein Self-Repulsive Dynamics: Benefits From Past Samples Mao Ye, Tongzheng Ren, Qiang Liu The Statistical Complexity of Early-Stopped Mirror Descent Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera Quantitative Propagation of Chaos for SGD in Wide Neural Networks Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli A Causal View on Robustness of Neural Networks Cheng Zhang, Kun Zhang, Yingzhen Li Minimax Classification with 0-1 Loss and Performance Guarantees Santiago Mazuelas, Andrea Zanoni, Aritz Pérez How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization Pierluca D'Oro, Wojciech Jaśkowski Coresets for Regressions with Panel Data Lingxiao Huang, K Sudhir, Nisheeth Vishnoi Learning Composable Energy Surrogates for PDE Order Reduction Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins Achieving Equalized Odds by Resampling Sensitive Attributes Yaniv Romano, Stephen Bates, Emmanuel Candes Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates Wenhao Luo, Wen Sun, Ashish Kapoor Hard Shape-Constrained Kernel Machines Pierre-Cyril Aubin-Frankowski, Zoltan Szabo 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 On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel Generalised Bayesian Filtering via Sequential Monte Carlo Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, Adam Johansen Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time Kai Han, zongmai Cao, Shuang Cui, Benwei Wu Flows for simultaneous manifold learning and density estimation Johann Brehmer, Kyle Cranmer Simultaneous Preference and Metric Learning from Paired Comparisons Austin Xu, Mark Davenport Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee Jincheng Bai, Qifan Song, Guang Cheng Learning Manifold Implicitly via Explicit Heat-Kernel Learning Yufan Zhou, Changyou Chen, Jinhui Xu Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou One-bit Supervision for Image Classification Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian What is being transferred in transfer learning? Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang Submodular Maximization Through Barrier Functions Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrak Neural Networks with Recurrent Generative Feedback Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Tsao, Anima Anandkumar Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Jinheon Baek, Dong Bok Lee, Sung Ju Hwang Exploiting weakly supervised visual patterns to learn from partial annotations Kaustav Kundu, Joseph Tighe Improving Inference for Neural Image Compression Yibo Yang, Robert Bamler, Stephan Mandt Neuron Merging: Compensating for Pruned Neurons Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A. Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing Arthur Delarue, Ross Anderson, Christian Tjandraatmadja Towards Playing Full MOBA Games with Deep Reinforcement Learning Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu Rankmax: An Adaptive Projection Alternative to the Softmax Function Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang Online Agnostic Boosting via Regret Minimization Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran Causal Intervention for Weakly-Supervised Semantic Segmentation Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun Belief Propagation Neural Networks Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora Post-training Iterative Hierarchical Data Augmentation for Deep Networks Adil Khan, Khadija Fraz Debugging Tests for Model Explanations Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim Robust compressed sensing using generative models Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model Alex Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob Foerster The route to chaos in routing games: When is price of anarchy too optimistic? Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, Georgios Piliouras Online Algorithm for Unsupervised Sequential Selection with Contextual Information Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvari, Venkatesh Saligrama Adapting Neural Architectures Between Domains Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu What went wrong and when? Instance-wise feature importance for time-series black-box models Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K. Duvenaud, Anna Goldenberg Towards Better Generalization of Adaptive Gradient Methods Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li Learning Guidance Rewards with Trajectory-space Smoothing Tanmay Gangwani, Yuan Zhou, Jian Peng Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song, Yong Jiang, Yi Ma Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding Rishi Sonthalia, Anna Gilbert Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker Convolutional Generation of Textured 3D Meshes Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi A Statistical Framework for Low-bitwidth Training of Deep Neural Networks Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez Better Set Representations For Relational Reasoning Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser Nam Lim, Austin R. Benson AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric Xing A Combinatorial Perspective on Transfer Learning Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness Hardness of Learning Neural Networks with Natural Weights Amit Daniely, Gal Vardi Higher-Order Spectral Clustering of Directed Graphs Steinar Laenen, He Sun Primal-Dual Mesh Convolutional Neural Networks Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto Watch out! Motion is Blurring the Vision of Your Deep Neural Networks Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu Sinkhorn Barycenter via Functional Gradient Descent Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani Coresets for Near-Convex Functions Murad Tukan, Alaa Maalouf, Dan Feldman Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh Improved Schemes for Episodic Memory-based Lifelong Learning Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing Adaptive Sampling for Stochastic Risk-Averse Learning Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring Jiangxin Dong, Stefan Roth, Bernt Schiele Discovering Reinforcement Learning Algorithms Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P. van Hasselt, Satinder Singh, David Silver Taming Discrete Integration via the Boon of Dimensionality Jeffrey Dudek, Dror Fried, Kuldeep S Meel Blind Video Temporal Consistency via Deep Video Prior Chenyang Lei, Yazhou Xing, Qifeng Chen Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin Model Selection for Production System via Automated Online Experiments Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher 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 Adaptation Properties Allow Identification of Optimized Neural Codes Luke Rast, Jan Drugowitsch Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang, Negar Kiyavash, Niao He Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity Kaiqing Zhang, Sham Kakade, Tamer Basar, Lin Yang Conservative Q-Learning for Offline Reinforcement Learning Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine Online Influence Maximization under Linear Threshold Model Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen Ensembling geophysical models with Bayesian Neural Networks Ushnish Sengupta, Matt Amos, Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul Young Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye, Colin Rowat, Ilya Feige Understanding Deep Architecture with Reasoning Layer Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song Planning in Markov Decision Processes with Gap-Dependent Sample Complexity Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill Detection as Regression: Certified Object Detection with Median Smoothing Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann FleXOR: Trainable Fractional Quantization Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun The Implications of Local Correlation on Learning Some Deep Functions Eran Malach, Shai Shalev-Shwartz Learning to search efficiently for causally near-optimal treatments Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses Ambar Pal, Rene Vidal Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier, Daniel Zügner, Stephan Günnemann Recurrent Quantum Neural Networks Johannes Bausch No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras A Unifying View of Optimism in Episodic Reinforcement Learning Gergely Neu, Ciara Pike-Burke Continuous Submodular Maximization: Beyond DR-Submodularity Moran Feldman, Amin Karbasi An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric Assessing SATNet's Ability to Solve the Symbol Grounding Problem Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger A Bayesian Nonparametrics View into Deep Representations Michał Jamroż, Marcin Kurdziel, Mateusz Opala On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman, Abhay Yadav, Yoni Kasten, Meirav Galun, David Jacobs, Basri Ronen A causal view of compositional zero-shot recognition Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik HiPPO: Recurrent Memory with Optimal Polynomial Projections Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré Auto Learning Attention Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono, Yao Zhang, Mihaela van der Schaar Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect Kaihua Tang, Jianqiang Huang, Hanwang Zhang Explainable Voting Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou Deep Archimedean Copulas Chun Kai Ling, Fei Fang, J. Zico Kolter Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi Thunder: a Fast Coordinate Selection Solver for Sparse Learning Shaogang Ren, Weijie Zhao, Ping Li Neural Networks Fail to Learn Periodic Functions and How to Fix It Liu Ziyin, Tilman Hartwig, Masahito Ueda Distribution Matching for Crowd Counting Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen Correspondence learning via linearly-invariant embedding Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi On Adaptive Attacks to Adversarial Example Defenses Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry Sinkhorn Natural Gradient for Generative Models Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani Online Sinkhorn: Optimal Transport distances from sample streams Arthur Mensch, Gabriel Peyré Ultrahyperbolic Representation Learning Marc Law, Jos Stam Locally-Adaptive Nonparametric Online Learning Ilja Kuzborskij, Nicolò Cesa-Bianchi Compositional Generalization via Neural-Symbolic Stack Machines Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou Graphon Neural Networks and the Transferability of Graph Neural Networks Luana Ruiz, Luiz Chamon, Alejandro Ribeiro Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction Michael Janner, Igor Mordatch, Sergey Levine Deep Transformers with Latent Depth Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows Kunal Gupta, Manmohan Chandraker Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees Haoran Zhu, Pavankumar Murali, Dzung Phan, Lam Nguyen, Jayant Kalagnanam Efficient Exact Verification of Binarized Neural Networks Kai Jia, Martin Rinard Ultra-Low Precision 4-bit Training of Deep Neural Networks Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, Tong Zhang On Numerosity of Deep Neural Networks Xi Zhang, Xiaolin Wu Outlier Robust Mean Estimation with Subgaussian Rates via Stability Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia Self-Supervised Relationship Probing Jiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad Morariu, Handong Zhao, Tong Sun Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, Yefeng Zheng Prophet Attention: Predicting Attention with Future Attention Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun Language Models are Few-Shot Learners Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei Margins are Insufficient for Explaining Gradient Boosting Allan Grønlund, Lior Kamma, Kasper Green Larsen Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics Alex Tseng, Avanti Shrikumar, Anshul Kundaje MomentumRNN: Integrating Momentum into Recurrent Neural Networks Tan Nguyen, Richard Baraniuk, Andrea Bertozzi, Stanley Osher, Bao Wang Marginal Utility for Planning in Continuous or Large Discrete Action Spaces Zaheen Ahmad, Levi Lelis, Michael Bowling Projected Stein Variational Gradient Descent Peng Chen, Omar Ghattas Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling On the equivalence of molecular graph convolution and molecular wave function with poor basis set Masashi Tsubaki, Teruyasu Mizoguchi The Power of Predictions in Online Control Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman Learning Affordance Landscapes for Interaction Exploration in 3D Environments Tushar Nagarajan, Kristen Grauman Cooperative Multi-player Bandit Optimization Ilai Bistritz, Nicholas Bambos Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne Haake Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward Guannan Qu, Yiheng Lin, Adam Wierman, Na Li Optimizing Neural Networks via Koopman Operator Theory Akshunna S. Dogra, William Redman SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet Adversarial Robustness of Supervised Sparse Coding Jeremias Sulam, Ramchandran Muthukumar, Raman Arora Differentiable Meta-Learning of Bandit Policies Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer Biologically Inspired Mechanisms for Adversarial Robustness Manish Vuyyuru Reddy, Andrzej Banburski, Nishka Pant, Tomaso Poggio Statistical-Query Lower Bounds via Functional Gradients Surbhi Goel, Aravind Gollakota, Adam Klivans Near-Optimal Reinforcement Learning with Self-Play Yu Bai, Chi Jin, Tiancheng Yu Network Diffusions via Neural Mean-Field Dynamics Shushan He, Hongyuan Zha, Xiaojing Ye Self-Distillation as Instance-Specific Label Smoothing Zhilu Zhang, Mert Sabuncu Towards Problem-dependent Optimal Learning Rates Yunbei Xu, Assaf Zeevi Cross-lingual Retrieval for Iterative Self-Supervised Training Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu Rethinking pooling in graph neural networks Diego Mesquita, Amauri Souza, Samuel Kaski Pointer Graph Networks Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell Gradient Regularized V-Learning for Dynamic Treatment Regimes Yao Zhang, Mihaela van der Schaar Faster Wasserstein Distance Estimation with the Sinkhorn Divergence Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré Forethought and Hindsight in Credit Assignment Veronica Chelu, Doina Precup, Hado P. van Hasselt Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar Rescuing neural spike train models from bad MLE Diego Arribas, Yuan Zhao, Il Memming Park Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtarik Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu Deep Imitation Learning for Bimanual Robotic Manipulation Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, Rose Yu Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen, Christabella Irwanto, Arno Solin Ensemble Distillation for Robust Model Fusion in Federated Learning Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi Falcon: Fast Spectral Inference on Encrypted Data Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang On Power Laws in Deep Ensembles Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov Practical Quasi-Newton Methods for Training Deep Neural Networks Donald Goldfarb, Yi Ren, Achraf Bahamou Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner, Justin Domke Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation Jianfeng Zhang, Xuecheng Nie, Jiashi Feng Consistent feature selection for analytic deep neural networks Vu C. Dinh, Lam S. Ho Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang Information Maximization for Few-Shot Learning Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed Inverse Reinforcement Learning from a Gradient-based Learner Giorgia Ramponi, Gianluca Drappo, Marcello Restelli Bayesian Multi-type Mean Field Multi-agent Imitation Learning Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong Bayesian Robust Optimization for Imitation Learning Daniel Brown, Scott Niekum, Marek Petrik Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, Yaron Lipman Riemannian Continuous Normalizing Flows Emile Mathieu, Maximilian Nickel Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation Isabella Pozzi, Sander Bohte, Pieter Roelfsema Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance Ziv Goldfeld, Kristjan Greenewald, Kengo Kato Online Robust Regression via SGD on the l1 loss Scott Pesme, Nicolas Flammarion PRANK: motion Prediction based on RANKing Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel Fighting Copycat Agents in Behavioral Cloning from Observation Histories Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model Raphaël Berthier, Francis Bach, Pierre Gaillard Structured Prediction for Conditional Meta-Learning Ruohan Wang, Yiannis Demiris, Carlo Ciliberto Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function Lingkai Kong, Molei Tao Identifying Learning Rules From Neural Network Observables Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. Yamins Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis Weakly-Supervised Reinforcement Learning for Controllable Behavior Lisa Lee, Ben Eysenbach, Russ R. 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