NeurIPS 2020

1,898 papers

Ensemble Distillation for Robust Model Fusion in Federated Learning

Tao LIN, Lingjing Kong, Sebastian U Stich, Martin Jaggi

Stationary Activations for Uncertainty Calibration in Deep Learning

Lassi Meronen, Christabella Irwanto, Arno Solin

Deep Imitation Learning for Bimanual Robotic Manipulation

Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtarik

Rescuing neural spike train models from bad MLE

Diego Arribas, Yuan Zhao, ll Memming Park

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee

Forethought and Hindsight in Credit Assignment

Veronica Chelu, Doina Precup, Hado P van Hasselt

Faster Wasserstein Distance Estimation with the Sinkhorn Divergence

Lénaïc Chizat, Pierre Roussillon, Flavien Léger, Francois-Xavier Vialard, Gabriel Peyré

Gradient Regularized V-Learning for Dynamic Treatment Regimes

Yao Zhang, Mihaela van der Schaar

Pointer Graph Networks

Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals

Rethinking pooling in graph neural networks

Diego Mesquita, Amauri Souza, Samuel Kaski

Cross-lingual Retrieval for Iterative Self-Supervised Training

Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu

Towards Problem-dependent Optimal Learning Rates

Yunbei Xu, Assaf J. Zeevi

Self-Distillation as Instance-Specific Label Smoothing

Zhilu Zhang, Mert Sabuncu

Network Diffusions via Neural Mean-Field Dynamics

Shushan He, Hongyuan Zha, Xiaojing Ye

Near-Optimal Reinforcement Learning with Self-Play

Yu Bai, Chi Jin, Tiancheng Yu

Statistical-Query Lower Bounds via Functional Gradients

Surbhi Goel, Aravind Gollakota, Adam Klivans

Biologically Inspired Mechanisms for Adversarial Robustness

Manish Reddy Vuyyuru, Andrzej Banburski, Nishka Pant, Tomaso Poggio

Differentiable Meta-Learning of Bandit Policies

Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári

Adversarial Robustness of Supervised Sparse Coding

Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence

Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet

Optimizing Neural Networks via Koopman Operator Theory

Akshunna S. Dogra, William Redman

Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward

Guannan Qu, Yiheng Lin, Adam Wierman, Na Li

Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains

Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne Haake

A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits

Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida

Cooperative Multi-player Bandit Optimization

Ilai Bistritz, Nicholas Bambos

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Tushar Nagarajan, Kristen Grauman

The Power of Predictions in Online Control

Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman

On the equivalence of molecular graph convolution and molecular wave function with poor basis set

Masashi Tsubaki, Teruyasu Mizoguchi

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling

Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks

Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi

Projected Stein Variational Gradient Descent

Peng Chen, Omar Ghattas

Marginal Utility for Planning in Continuous or Large Discrete Action Spaces

Zaheen Ahmad, Levi Lelis, Michael Bowling

MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Tan Nguyen, Richard Baraniuk, Andrea Bertozzi, Stanley Osher, Bao Wang

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics

Alex Tseng, Avanti Shrikumar, Anshul Kundaje

Margins are Insufficient for Explaining Gradient Boosting

Allan Grønlund, Lior Kamma, Kasper Green Larsen

Language Models are Few-Shot Learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan

Prophet Attention: Predicting Attention with Future Attention

Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu

Self-Supervised Relationship Probing

Jiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad I. Morariu

Outlier Robust Mean Estimation with Subgaussian Rates via Stability

Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia

On Numerosity of Deep Neural Networks

Xi Zhang, Xiaolin Wu

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok

Ultra-Low Precision 4-bit Training of Deep Neural Networks

Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal

Efficient Exact Verification of Binarized Neural Networks

Kai Jia, Martin Rinard

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees

Haoran Zhu, Pavankumar Murali, Dzung Phan, Lam Nguyen, Jayant Kalagnanam

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso

Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion

Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows

Kunal Gupta, Manmohan Chandraker

Deep Transformers with Latent Depth

Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

Michael Janner, Igor Mordatch, Sergey Levine

Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi

Graphon Neural Networks and the Transferability of Graph Neural Networks

Luana Ruiz, Luiz Chamon, Alejandro Ribeiro

Compositional Generalization via Neural-Symbolic Stack Machines

Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou

Locally-Adaptive Nonparametric Online Learning

Ilja Kuzborskij, Nicolò Cesa-bianchi

Ultrahyperbolic Representation Learning

Marc Law, Jos Stam

Online Sinkhorn: Optimal Transport distances from sample streams

Arthur Mensch, Gabriel Peyré

Sinkhorn Natural Gradient for Generative Models

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

On Adaptive Attacks to Adversarial Example Defenses

Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan

Correspondence learning via linearly-invariant embedding

Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov

Distribution Matching for Crowd Counting

Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen

Neural Networks Fail to Learn Periodic Functions and How to Fix It

Liu Ziyin, Tilman Hartwig, Masahito Ueda

Thunder: a Fast Coordinate Selection Solver for Sparse Learning

Shaogang Ren, Weijie Zhao, Ping Li

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy

Deep Archimedean Copulas

Chun Kai Ling, Fei Fang, J. Zico Kolter

Explainable Voting

Dominik Peters, Ariel D Procaccia, Alexandros Psomas, Zixin Zhou

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

Kaihua Tang, Jianqiang Huang, Hanwang Zhang

CASTLE: Regularization via Auxiliary Causal Graph Discovery

Trent Kyono, Yao Zhang, Mihaela van der Schaar

Auto Learning Attention

Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao

HiPPO: Recurrent Memory with Optimal Polynomial Projections

Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re

A causal view of compositional zero-shot recognition

Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

Amnon Geifman, Abhay Yadav, Yoni Kasten, Meirav Galun, David Jacobs

A Bayesian Nonparametrics View into Deep Representations

Michał Jamroż, Marcin Kurdziel, Mateusz Opala

Assessing SATNet's Ability to Solve the Symbol Grounding Problem

Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric

Continuous Submodular Maximization: Beyond DR-Submodularity

Moran Feldman, Amin Karbasi

A Unifying View of Optimism in Episodic Reinforcement Learning

Gergely Neu, Ciara Pike-Burke

No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix

Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras

Recurrent Quantum Neural Networks

Johannes Bausch

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses

Ambar Pal, René Vidal

Learning to search efficiently for causally near-optimal treatments

Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D Johansson

The Implications of Local Correlation on Learning Some Deep Functions

Eran Malach, Shai Shalev-shwartz

FleXOR: Trainable Fractional Quantization

Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park

ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks

Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

Detection as Regression: Certified Object Detection with Median Smoothing

Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity

Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent

Understanding Deep Architecture with Reasoning Layer

Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability

Christopher Frye, Colin Rowat, Ilya Feige

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin

Ensembling geophysical models with Bayesian Neural Networks

Ushnish Sengupta, Matt Amos, Scott Hosking, Carl Edward Rasmussen, Matthew Juniper

Online Influence Maximization under Linear Threshold Model

Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen

Conservative Q-Learning for Offline Reinforcement Learning

Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity

Kaiqing Zhang, Sham Kakade, Tamer Basar, Lin Yang