NeurIPS 2020

1,898 papers

Stochastic Normalizing Flows

Hao Wu, Jonas Köhler, Frank Noe

Neuron Shapley: Discovering the Responsible Neurons

Amirata Ghorbani, James Y Zou

On Second Order Behaviour in Augmented Neural ODEs

Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió

Evaluating Attribution for Graph Neural Networks

Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

Junsouk Choi, Robert Chapkin, Yang Ni

Stochastic Deep Gaussian Processes over Graphs

Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang

Graph Meta Learning via Local Subgraphs

Kexin Huang, Marinka Zitnik

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman

Gradient Surgery for Multi-Task Learning

Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman

Graph Contrastive Learning with Augmentations

Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang

Woodbury Transformations for Deep Generative Flows

You Lu, Bert Huang

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood

On Testing of Samplers

Kuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty

Bayesian Bits: Unifying Quantization and Pruning

Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Yue Wu, Pan Zhou, Andrew G Wilson, Eric P. Xing, Zhiting Hu

SLIP: Learning to predict in unknown dynamical systems with long-term memory

Paria Rashidinejad, Jiantao Jiao, Stuart J. Russell

Meta-Learning Requires Meta-Augmentation

Janarthanan Rajendran, Alexander Irpan, Eric Jang

Gradient Estimation with Stochastic Softmax Tricks

Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J Maddison

Self-supervised Co-Training for Video Representation Learning

Tengda Han, Weidi Xie, Andrew Zisserman

A Catalyst Framework for Minimax Optimization

Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He

Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views

Nanbo Li, Cian Eastwood, Robert Fisher

Entrywise convergence of iterative methods for eigenproblems

Vasileios Charisopoulos, Austin R Benson, Anil Damle

Unfolding the Alternating Optimization for Blind Super Resolution

zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

RepPoints v2: Verification Meets Regression for Object Detection

Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin

Learning of Discrete Graphical Models with Neural Networks

Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra, Marc Vuffray

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue

Policy Improvement via Imitation of Multiple Oracles

Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks

Jingqiu Ding, Samuel Hopkins, David Steurer

Latent World Models For Intrinsically Motivated Exploration

Aleksandr Ermolov, Nicu Sebe

Structured Convolutions for Efficient Neural Network Design

Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli

The Value Equivalence Principle for Model-Based Reinforcement Learning

Christopher Grimm, Andre Barreto, Satinder P. Singh, David Silver

Independent Policy Gradient Methods for Competitive Reinforcement Learning

Constantinos Daskalakis, Dylan J Foster, Noah Golowich

Towards practical differentially private causal graph discovery

Lun Wang, Qi Pang, Dawn Song

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner

Language Through a Prism: A Spectral Approach for Multiscale Language Representations

Alex Tamkin, Dan Jurafsky, Noah Goodman

Learning under Model Misspecification: Applications to Variational and Ensemble methods

Andres Masegosa

Sub-sampling for Efficient Non-Parametric Bandit Exploration

Dorian Baudry, Emilie Kaufmann, Odalric-ambrym Maillard

Generative causal explanations of black-box classifiers

Matthew O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark Davenport

Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry

Yossi Arjevani, Michael Field

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits

Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet

Agnostic Learning of a Single Neuron with Gradient Descent

Spencer Frei, Yuan Cao, Quanquan Gu

Parabolic Approximation Line Search for DNNs

Maximus Mutschler, Andreas Zell

Adam with Bandit Sampling for Deep Learning

Rui Liu, Tianyi Wu, Barzan Mozafari

Learning to Orient Surfaces by Self-supervised Spherical CNNs

Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm

Tianyi Lin, nhật Hồ, Xi Chen, Marco Cuturi, Michael I. Jordan

An analytic theory of shallow networks dynamics for hinge loss classification

Franco Pellegrini, Giulio Biroli

Robust Density Estimation under Besov IPM Losses

Ananya Uppal, Shashank Singh, Barnabás Póczos

Federated Accelerated Stochastic Gradient Descent

Honglin Yuan, Tengyu Ma

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato

Robust Quantization: One Model to Rule Them All

moran shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Javen Qinfeng Shi, Anton van den Hengel

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell

Dirichlet Graph Variational Autoencoder

Jia Li, Jianwei Yu, JIAJIN LI, Honglei Zhang, Kangfei Zhao

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote

Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin

Sparse Graphical Memory for Robust Planning

Scott Emmons, Ajay N. Jain, Misha Laskin, Thanard Kurutach, Pieter Abbeel

Multiscale Deep Equilibrium Models

Shaojie Bai, Vladlen Koltun, J. Zico Kolter

Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning

Chandrashekar Lakshminarayanan, Amit Vikram Singh

A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling

Xiaotie Deng, Ron Lavi, Tao LIN, Qi Qi, Wenwei WANG

Noise-Contrastive Estimation for Multivariate Point Processes

Hongyuan Mei, Tom Wan, Jason Eisner

Robust Optimization for Fairness with Noisy Protected Groups

Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

Learning Physical Constraints with Neural Projections

Shuqi Yang, Xingzhe He, Bo Zhu

Certifying Confidence via Randomized Smoothing

Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein

Exact expressions for double descent and implicit regularization via surrogate random design

Michal Derezinski, Feynman T Liang, Michael W. Mahoney

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A Erdogdu

Model Inversion Networks for Model-Based Optimization

Aviral Kumar, Sergey Levine

Causal Estimation with Functional Confounders

Aahlad Puli, Adler J. Perotte, Rajesh Ranganath

Feature Importance Ranking for Deep Learning

Maksymilian Wojtas, Ke Chen

Attribution Preservation in Network Compression for Reliable Network Interpretation

Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang

Decision-Making with Auto-Encoding Variational Bayes

Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier

A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons

Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora

Neural Dynamic Policies for End-to-End Sensorimotor Learning

Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak

Meta-Neighborhoods

Siyuan Shan, Yang Li, Junier B Oliva

Deep Direct Likelihood Knockoffs

Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Meng Liu, David F Gleich

HOI Analysis: Integrating and Decomposing Human-Object Interaction

Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu

Continual Learning of Control Primitives : Skill Discovery via Reset-Games

Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine

Certifying Strategyproof Auction Networks

Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson

Network size and size of the weights in memorization with two-layers neural networks

Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer

Domain Adaptation as a Problem of Inference on Graphical Models

Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, QINGSONG LIU

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method

Michal Derezinski, Rajiv Khanna, Michael W. Mahoney

Learning Differentiable Programs with Admissible Neural Heuristics

Ameesh Shah, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue

Stochastic Optimization for Performative Prediction

Celestine Mendler-Dünner, Juan Perdomo, Tijana Zrnic, Moritz Hardt

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen

Telescoping Density-Ratio Estimation

Benjamin Rhodes, Kai Xu, Michael U. Gutmann

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons

Qianyi Li, Cengiz Pehlevan

Probabilistic Orientation Estimation with Matrix Fisher Distributions

David Mohlin, Josephine Sullivan, Gérald Bianchi

Delay and Cooperation in Nonstochastic Linear Bandits

Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu

Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems

Mayalen Etcheverry, Clément Moulin-Frier, Pierre-yves Oudeyer

Deep Multimodal Fusion by Channel Exchanging

Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

Feng Liu, Xiaoming Liu

Deep Variational Instance Segmentation

Jialin Yuan, Chao Chen, Fuxin Li

Improved Algorithms for Convex-Concave Minimax Optimization

Yuanhao Wang, Jian Li

On the training dynamics of deep networks with L_2 regularization

Aitor Lewkowycz, Guy Gur-Ari

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen

Multi-Task Reinforcement Learning with Soft Modularization

Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang

Towards More Practical Adversarial Attacks on Graph Neural Networks

Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei