NeurIPS 2020

1,898 papers

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Junchi Yang, Negar Kiyavash, Niao He

Adaptation Properties Allow Identification of Optimized Neural Codes

Luke Rast, Jan Drugowitsch

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher

Model Selection for Production System via Automated Online Experiments

Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin

Blind Video Temporal Consistency via Deep Video Prior

Chenyang Lei, Yazhou Xing, Qifeng Chen

Taming Discrete Integration via the Boon of Dimensionality

Jeffrey Dudek, Dror Fried, Kuldeep S Meel

Discovering Reinforcement Learning Algorithms

Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P van Hasselt

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

Jiangxin Dong, Stefan Roth, Bernt Schiele

Adaptive Sampling for Stochastic Risk-Averse Learning

Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause

Improved Schemes for Episodic Memory-based Lifelong Learning

Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

Bayesian Deep Ensembles via the Neural Tangent Kernel

Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

Coresets for Near-Convex Functions

Murad Tukan, Alaa Maalouf, Dan Feldman

Sinkhorn Barycenter via Functional Gradient Descent

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning

Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto

Primal-Dual Mesh Convolutional Neural Networks

Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone

Higher-Order Spectral Clustering of Directed Graphs

Steinar Laenen, He Sun

Hardness of Learning Neural Networks with Natural Weights

Amit Daniely, Gal Vardi

A Combinatorial Perspective on Transfer Learning

Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness

AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning

Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin

Better Set Representations For Relational Reasoning

Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser Nam Lim

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks

Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E Gonzalez

Convolutional Generation of Textured 3D Meshes

Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi

Deep Structural Causal Models for Tractable Counterfactual Inference

Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker

Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding

Rishi Sonthalia, Anna Gilbert

Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

Chaobing Song, Yong Jiang, Yi Ma

Learning Guidance Rewards with Trajectory-space Smoothing

Tanmay Gangwani, Yuan Zhou, Jian Peng

Towards Better Generalization of Adaptive Gradient Methods

Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li

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

Adapting Neural Architectures Between Domains

Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu

Online Algorithm for Unsupervised Sequential Selection with Contextual Information

Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama

The route to chaos in routing games: When is price of anarchy too optimistic?

Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, Georgios Piliouras

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine

Fairness without Demographics through Adversarially Reweighted Learning

Preethi Lahoti, Alex Beutel, Jilin Chen, Flavien Prost, Nithum Thain

Robust compressed sensing using generative models

Ajil Jalal, Liu Liu, Alexandros G Dimakis, Constantine Caramanis

Debugging Tests for Model Explanations

Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim

Post-training Iterative Hierarchical Data Augmentation for Deep Networks

Adil Khan, Khadija Fraz

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality

Yi Zhang, Orestis Plevrakis, Simon S Du, Xingguo Li, Zhao Song

Belief Propagation Neural Networks

Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song

Causal Intervention for Weakly-Supervised Semantic Segmentation

Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Online Agnostic Boosting via Regret Minimization

Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran

Rankmax: An Adaptive Projection Alternative to the Softmax Function

Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang

Towards Playing Full MOBA Games with Deep Reinforcement Learning

Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang

Neuron Merging: Compensating for Pruned Neurons

Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon

Improving Inference for Neural Image Compression

Yibo Yang, Robert Bamler, Stephan Mandt

Exploiting weakly supervised visual patterns to learn from partial annotations

Kaustav Kundu, Joseph Tighe

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

Jinheon Baek, Dong Bok Lee, Sung Ju Hwang

Neural Networks with Recurrent Generative Feedback

Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen

Submodular Maximization Through Barrier Functions

Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrak

What is being transferred in transfer learning?

Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

One-bit Supervision for Image Classification

Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network

Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang

Learning Manifold Implicitly via Explicit Heat-Kernel Learning

Yufan Zhou, Changyou Chen, Jinhui Xu

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee

Jincheng Bai, Qifan Song, Guang Cheng

Simultaneous Preference and Metric Learning from Paired Comparisons

Austin Xu, Mark Davenport

Flows for simultaneous manifold learning and density estimation

Johann Brehmer, Kyle Cranmer

Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time

Kai Han, zongmai Cao, Shuang Cui, Benwei Wu

Generalised Bayesian Filtering via Sequential Monte Carlo

Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, Adam Johansen

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan

A Closer Look at the Training Strategy for Modern Meta-Learning

JIAXIN CHEN, Xiao-ming Wu, Yanke Li, Qimai LI, Li-Ming Zhan

Hard Shape-Constrained Kernel Machines

Pierre-Cyril Aubin-Frankowski, Zoltan Szabo

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Wenhao Luo, Wen Sun, Ashish Kapoor

Achieving Equalized Odds by Resampling Sensitive Attributes

Yaniv Romano, Stephen Bates, Emmanuel Candes

Efficient Contextual Bandits with Continuous Actions

Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford

Learning Composable Energy Surrogates for PDE Order Reduction

Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

Coresets for Regressions with Panel Data

Lingxiao Huang, K Sudhir, Nisheeth Vishnoi

How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

Pierluca D'Oro, Wojciech Jaśkowski

Minimax Classification with 0-1 Loss and Performance Guarantees

Santiago Mazuelas, Andrea Zanoni, Aritz Pérez

A Causal View on Robustness of Neural Networks

Cheng Zhang, Kun Zhang, Yingzhen Li

Quantitative Propagation of Chaos for SGD in Wide Neural Networks

Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

The Statistical Complexity of Early-Stopped Mirror Descent

Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini

Stein Self-Repulsive Dynamics: Benefits From Past Samples

Mao Ye, Tongzheng Ren, Qiang Liu

Statistical Guarantees of Distributed Nearest Neighbor Classification

Jiexin Duan, Xingye Qiao, Guang Cheng

Reciprocal Adversarial Learning via Characteristic Functions

Shengxi Li, Zeyang Yu, Min Xiang, Danilo P. Mandic

Deep reconstruction of strange attractors from time series

William Gilpin

Permute-and-Flip: A new mechanism for differentially private selection

Ryan McKenna, Daniel R. Sheldon

Improving Local Identifiability in Probabilistic Box Embeddings

Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li

Cascaded Text Generation with Markov Transformers

Yuntian Deng, Alexander Rush

Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering

Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong

Adversarially Robust Streaming Algorithms via Differential Privacy

Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Synbols: Probing Learning Algorithms with Synthetic Datasets

Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds

Nicholas Harvey, Christopher Liaw, Tasuku Soma

Fourier Sparse Leverage Scores and Approximate Kernel Learning

Tamas Erdelyi, Cameron Musco, Christopher Musco

PyGlove: Symbolic Programming for Automated Machine Learning

Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu

Backpropagating Linearly Improves Transferability of Adversarial Examples

Yiwen Guo, Qizhang Li, Hao Chen

Fast and Flexible Temporal Point Processes with Triangular Maps

Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann

Neural Methods for Point-wise Dependency Estimation

Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

Masatoshi Uehara, Masahiro Kato, Shota Yasui

Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method

Simiao Ren, Willie Padilla, Jordan Malof

Self-Supervised MultiModal Versatile Networks

Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram

An Unsupervised Information-Theoretic Perceptual Quality Metric

Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen

A graph similarity for deep learning

Seongmin Ok