NeurIPS 2020

1,898 papers

No-regret Learning in Price Competitions under Consumer Reference Effects

Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang

Avoiding Side Effects in Complex Environments

Alex Turner, Neale Ratzlaff, Prasad Tadepalli

Personalized Federated Learning with Moreau Envelopes

Canh T. Dinh, Nguyen Tran, Josh Nguyen

A Topological Filter for Learning with Label Noise

Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, Chao Chen

On the distance between two neural networks and the stability of learning

Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu

ContraGAN: Contrastive Learning for Conditional Image Generation

Minguk Kang, Jaesik Park

How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?

Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe

The Statistical Cost of Robust Kernel Hyperparameter Turning

Raphael Meyer, Christopher Musco

A Group-Theoretic Framework for Data Augmentation

Shuxiao Chen, Edgar Dobriban, Jane Lee

Efficient Clustering for Stretched Mixtures: Landscape and Optimality

Kaizheng Wang, Yuling Yan, Mateo Diaz

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning

Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu Hong Hoi

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning

Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality

Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

Junhyung Park, Krikamol Muandet

Sharper Generalization Bounds for Pairwise Learning

Yunwen Lei, Antoine Ledent, Marius Kloft

Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization

Qi Chen, Lin Sun, Ernest Cheung, Alan L. Yuille

Learning to Learn with Feedback and Local Plasticity

Jack Lindsey, Ashok Litwin-Kumar

Uncertainty-aware Self-training for Few-shot Text Classification

Subhabrata Mukherjee, Ahmed Awadallah

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon

Implicit Regularization in Deep Learning May Not Be Explainable by Norms

Noam Razin, Nadav Cohen

Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking

Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari

On Convergence and Generalization of Dropout Training

Poorya Mianjy, Raman Arora

Regret in Online Recommendation Systems

Kaito Ariu, Narae Ryu, Se-Young Yun, Alexandre Proutiere

Escaping the Gravitational Pull of Softmax

Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment

Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko

Hypersolvers: Toward Fast Continuous-Depth Models

Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park

WOR and p's: Sketches for ℓ_p-Sampling Without Replacement

Edith Cohen, Rasmus Pagh, David Woodruff

CircleGAN: Generative Adversarial Learning across Spherical Circles

Woohyeon Shim, Minsu Cho

Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions

Shom Banerjee

Self-training Avoids Using Spurious Features Under Domain Shift

Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma

Fair Hierarchical Clustering

Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, mingyan liu

Learning by Minimizing the Sum of Ranked Range

Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu

Sparse and Continuous Attention Mechanisms

Andre Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro Aguiar

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula

Yuxuan Zhao, Madeleine Udell

Learning discrete distributions: user vs item-level privacy

Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Michael Riley

Train-by-Reconnect: Decoupling Locations of Weights from Their Values

Yushi Qiu, Reiji Suda

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

Noémie Jaquier, Leonel Rozo

The Cone of Silence: Speech Separation by Localization

Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman

A Self-Tuning Actor-Critic Algorithm

Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction

David Novotny, Roman Shapovalov, Andrea Vedaldi

Linearly Converging Error Compensated SGD

Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtarik

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

Gradient-EM Bayesian Meta-Learning

Yayi Zou, Xiaoqi Lu

The Generalization-Stability Tradeoff In Neural Network Pruning

Brian Bartoldson, Ari Morcos, Adrian Barbu, Gordon Erlebacher

Adversarial Attacks on Deep Graph Matching

Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin

Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher

Guangda Ji, Zhanxing Zhu

Probabilistic Active Meta-Learning

Jean Kaddour, Steindor Saemundsson, Marc Deisenroth (he/him)

Statistical and Topological Properties of Sliced Probability Divergences

Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

Nathan Inkawhich, Kevin Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin

Curvature Regularization to Prevent Distortion in Graph Embedding

Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang

Tight last-iterate convergence rates for no-regret learning in multi-player games

Noah Golowich, Sarath Pattathil, Constantinos Daskalakis

Meta-Learning with Adaptive Hyperparameters

Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee

Top-KAST: Top-K Always Sparse Training

Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, Erich Elsen

A Computational Separation between Private Learning and Online Learning

Mark Bun

Meta-Learning through Hebbian Plasticity in Random Networks

Elias Najarro, Sebastian Risi

Finding All ε-Good Arms in Stochastic Bandits

Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak

Deep Diffusion-Invariant Wasserstein Distributional Classification

Sung Woo Park, Dong Wook Shu, Junseok Kwon

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder

Zhisheng Xiao, Qing Yan, Yali Amit

Learning from Failure: De-biasing Classifier from Biased Classifier

Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, Jinwoo Shin

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method

Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka

Online Optimization with Memory and Competitive Control

Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman

Optimally Deceiving a Learning Leader in Stackelberg Games

Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco Marmolejo, Ninad Rajgopal

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang

Unsupervised Translation of Programming Languages

Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample

Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay

Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Polina Kirichenko, Pavel Izmailov, Andrew G Wilson

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems

Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang

On Completeness-aware Concept-Based Explanations in Deep Neural Networks

Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister

A Limitation of the PAC-Bayes Framework

Roi Livni, Shay Moran

Information-theoretic Task Selection for Meta-Reinforcement Learning

Ricardo Luna Gutierrez, Matteo Leonetti

Differentiable Top-k with Optimal Transport

Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin

3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data

Benjamin Biggs, David Novotny, Sebastien Ehrhardt, Hanbyul Joo, Ben Graham

Agnostic Learning with Multiple Objectives

Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference

Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain

Adaptive Online Estimation of Piecewise Polynomial Trends

Dheeraj Baby, Yu-Xiang Wang

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise

Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

Graph Information Bottleneck

Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec

Online Learning with Primary and Secondary Losses

Avrim Blum, Han Shao

Position-based Scaled Gradient for Model Quantization and Pruning

Jangho Kim, KiYoon Yoo, Nojun Kwak

Online Matrix Completion with Side Information

Mark Herbster, Stephen Pasteris, Lisa Tse

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot

Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao

Movement Pruning: Adaptive Sparsity by Fine-Tuning

Victor Sanh, Thomas Wolf, Alexander Rush

Consistent Plug-in Classifiers for Complex Objectives and Constraints

Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan

Learning Parities with Neural Networks

Amit Daniely, Eran Malach

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond

Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu

Early-Learning Regularization Prevents Memorization of Noisy Labels

Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Yaochen Xie, Zhengyang Wang, Shuiwang Ji

A Study on Encodings for Neural Architecture Search

Colin White, Willie Neiswanger, Sam Nolen, Yash Savani

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R

Factorizable Graph Convolutional Networks

Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang

Instance Based Approximations to Profile Maximum Likelihood

Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford

Latent Template Induction with Gumbel-CRFs

Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng

Improving Sparse Vector Technique with Renyi Differential Privacy

Yuqing Zhu, Yu-Xiang Wang

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Stephen Mcaleer, JB Lanier, Roy Fox, Pierre Baldi