NeurIPS 2020

1,898 papers

Optimal Learning from Verified Training Data

Nicholas Bishop, Long Tran-Thanh, Enrico Gerding

Learning with Differentiable Pertubed Optimizers

Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-philippe Vert

A Boolean Task Algebra for Reinforcement Learning

Geraud Nangue Tasse, Steven James, Benjamin Rosman

Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation

Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon

Universal guarantees for decision tree induction via a higher-order splitting criterion

Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin

Color Visual Illusions: A Statistics-based Computational Model

Elad Hirsch, Ayellet Tal

Learning Rich Rankings

Arjun Seshadri, Stephen Ragain, Johan Ugander

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma

Simple and Fast Algorithm for Binary Integer and Online Linear Programming

Xiaocheng Li, Chunlin Sun, Yinyu Ye

CoinDICE: Off-Policy Confidence Interval Estimation

Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári

Projection Robust Wasserstein Distance and Riemannian Optimization

Tianyi Lin, Chenyou Fan, nhật Hồ, Marco Cuturi, Michael I. Jordan

Minimax Bounds for Generalized Linear Models

Kuan-Yun Lee, Thomas Courtade

Bayesian Optimization for Iterative Learning

Vu Nguyen, Sebastian Schulze, Michael Osborne

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala

Steady State Analysis of Episodic Reinforcement Learning

Huang Bojun

Exact Recovery of Mangled Clusters with Same-Cluster Queries

Marco Bressan, Nicolò Cesa-bianchi, Silvio Lattanzi, Andrea Paudice

List-Decodable Mean Estimation via Iterative Multi-Filtering

Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard

Towards Convergence Rate Analysis of Random Forests for Classification

Wei Gao, Zhi-Hua Zhou

Optimal visual search based on a model of target detectability in natural images

Shima Rashidi, Krista Ehinger, Andrew Turpin, Lars Kulik

Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction

Tong He, John Collomosse, Hailin Jin, Stefano Soatto

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

Xiang Zhang, Marinka Zitnik

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples

Jay Nandy, Wynne Hsu, Mong Li Lee

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

Hu Liu, Jing LU, Xiwei Zhao, Sulong Xu, Hao Peng

Self-Paced Deep Reinforcement Learning

Pascal Klink, Carlo D'Eramo, Joni K. Pajarinen, Jan R. Peters, Jan R Peters

Smoothed Analysis of Online and Differentially Private Learning

Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang

Causal Discovery in Physical Systems from Videos

Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg

Finer Metagenomic Reconstruction via Biodiversity Optimization

Simon Foucart, David Koslicki

Towards Safe Policy Improvement for Non-Stationary MDPs

Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Philip S. Thomas

Learnability with Indirect Supervision Signals

Kaifu Wang, Qiang Ning, Dan Roth

Myersonian Regression

Allen Liu, Renato Leme, Jon Schneider

Learning to Learn Variational Semantic Memory

Xiantong Zhen, Yingjun Du, Huan Xiong, Qiang Qiu, Cees Snoek

Learning Mutational Semantics

Brian Hie, Ellen Zhong, Bryan Bryson, Bonnie Berger

Adversarial Learning for Robust Deep Clustering

Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

Is Long Horizon RL More Difficult Than Short Horizon RL?

Ruosong Wang, Simon S Du, Lin Yang, Sham Kakade

Matrix Completion with Hierarchical Graph Side Information

Adel Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer

On Regret with Multiple Best Arms

Yinglun Zhu, Robert Nowak

Chaos, Extremism and Optimism: Volume Analysis of Learning in Games

Yun Kuen Cheung, Georgios Piliouras

Towards a Combinatorial Characterization of Bounded-Memory Learning

Alon Gonen, Shachar Lovett, Michal Moshkovitz

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits

Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov

Neural Networks Learning and Memorization with (almost) no Over-Parameterization

Amit Daniely

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

Further Analysis of Outlier Detection with Deep Generative Models

Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu

Learning to Play Sequential Games versus Unknown Opponents

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause

Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

Pinar Ozisik, Philip S. Thomas

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach

Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts

Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan

Adversarial Example Games

Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent

Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View

CHRISTOS THRAMPOULIDIS, Samet Oymak, Mahdi Soltanolkotabi

PEP: Parameter Ensembling by Perturbation

Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann

Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals

Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su

Bandit Linear Control

Asaf Cassel, Tomer Koren

Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring

Taira Tsuchiya, Junya Honda, Masashi Sugiyama

Large-Scale Methods for Distributionally Robust Optimization

Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

Better Full-Matrix Regret via Parameter-Free Online Learning

Ashok Cutkosky

Understanding spiking networks through convex optimization

Allan Mancoo, Sander Keemink, Christian K. Machens

Impossibility Results for Grammar-Compressed Linear Algebra

Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann

COT-GAN: Generating Sequential Data via Causal Optimal Transport

Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio

Generalized Boosting

Arun Suggala, Bingbin Liu, Pradeep K Ravikumar, Pradeep K. Ravikumar

UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree

Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz

Debiased Contrastive Learning

Ching-Yao Chuang, Joshua W. Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka

Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation

Sajad Norouzi, David J Fleet, Mohammad Norouzi

Approximate Cross-Validation for Structured Models

Soumya Ghosh, Will Stephenson, Tin D Nguyen, Sameer Deshpande, Tamara Broderick

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Ximeng Sun, Rameswar Panda, Rogerio Feris, Kate Saenko

Sliding Window Algorithms for k-Clustering Problems

Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs

Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models

Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

Adaptive Probing Policies for Shortest Path Routing

Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation

Rasool Fakoor, Jonas W Mueller, Nick Erickson, Pratik Chaudhari, Alexander J Smola

Characterizing emergent representations in a space of candidate learning rules for deep networks

Yinan Cao, Christopher Summerfield, Andrew Saxe

Baxter Permutation Process

Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Qian Lou, Song Bian, Lei Jiang

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

David Bieber, Charles A. Sutton, Hugo Larochelle, Daniel Tarlow

Spin-Weighted Spherical CNNs

Carlos Esteves, Ameesh Makadia, Kostas Daniilidis

Curriculum Learning by Dynamic Instance Hardness

Tianyi Zhou, Shengjie Wang, Jeffrey Bilmes

A Closer Look at Accuracy vs. Robustness

Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri

Factor Graph Neural Networks

Zhen Zhang, Fan Wu, Wee Sun Lee

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe

Sanghack Lee, Elias Bareinboim

Optimal Prediction of the Number of Unseen Species with Multiplicity

Yi Hao, Ping Li

Bad Global Minima Exist and SGD Can Reach Them

Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas

PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

Henry Charlesworth, Giovanni Montana

Multi-Fidelity Bayesian Optimization via Deep Neural Networks

Shibo Li, Wei Xing, Robert Kirby, Shandian Zhe

Offline Imitation Learning with a Misspecified Simulator

Shengyi Jiang, Jingcheng Pang, Yang Yu

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties

Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch

Targeted Adversarial Perturbations for Monocular Depth Prediction

Alex Wong, Safa Cicek, Stefano Soatto

Zero-Resource Knowledge-Grounded Dialogue Generation

Linxiao Li, Can Xu, Wei Wu, YUFAN ZHAO, Xueliang Zhao

Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks

Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang

Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards

Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh

General Control Functions for Causal Effect Estimation from IVs

Aahlad Puli, Rajesh Ranganath

Reparameterizing Mirror Descent as Gradient Descent

Ehsan Amid, Manfred K. Warmuth

Estimation of Skill Distribution from a Tournament

Ali Jadbabaie, Anuran Makur, Devavrat Shah

Certified Defense to Image Transformations via Randomized Smoothing

Marc Fischer, Maximilian Baader, Martin Vechev

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability

Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau

Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes

Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo Jovanovic

Reinforced Molecular Optimization with Neighborhood-Controlled Grammars

Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang

Finding the Homology of Decision Boundaries with Active Learning

Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-gon Chun