NeurIPS 2020

1,898 papers

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift

Zayd Hammoudeh, Daniel Lowd

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox

A Spectral Energy Distance for Parallel Speech Synthesis

Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart J. Russell

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Tao Fang, Yu Qi, Gang Pan

EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints

Sachin Chauhan, Kashish Bansal, Rijurekha Sen

Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network

Lifeng Shen, Zhuocong Li, James T. Kwok

The phase diagram of approximation rates for deep neural networks

Dmitry Yarotsky, Anton Zhevnerchuk

Sample complexity and effective dimension for regression on manifolds

Andrew McRae, Justin Romberg, Mark Davenport

CompRess: Self-Supervised Learning by Compressing Representations

Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin

Debiasing Averaged Stochastic Gradient Descent to handle missing values

Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse

Autofocused oracles for model-based design

Clara Fannjiang, Jennifer Listgarten

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation

Yogesh Balaji, Rama Chellappa, Soheil Feizi

Fully Dynamic Algorithm for Constrained Submodular Optimization

Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakub M Tarnawski, Morteza Zadimoghaddam

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

Reward Propagation Using Graph Convolutional Networks

Martin Klissarov, Doina Precup

Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks

Alexander Shekhovtsov, Viktor Yanush, Boris Flach

Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization

Hassan Mortagy, Swati Gupta, Sebastian Pokutta

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen

Practical No-box Adversarial Attacks against DNNs

Qizhang Li, Yiwen Guo, Hao Chen

ConvBERT: Improving BERT with Span-based Dynamic Convolution

Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng

Uncertainty Aware Semi-Supervised Learning on Graph Data

Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

Non-parametric Models for Non-negative Functions

Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi

f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning

Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang

CogLTX: Applying BERT to Long Texts

Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang

Testing Determinantal Point Processes

Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

Ilyes Khemakhem, Ricardo Monti, Diederik Kingma, Aapo Hyvärinen

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques

Probabilistic Fair Clustering

Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson

Multi-Stage Influence Function

Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, Finale Doshi-velez

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes

Minh Hoang, Nghia Hoang, Hai Pham, David Woodruff

Learning Causal Effects via Weighted Empirical Risk Minimization

Yonghan Jung, Jin Tian, Elias Bareinboim

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz

Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models

Adarsh Keshav Jeewajee, Leslie P. Kaelbling

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time

Jerry Li, Guanghao Ye

Joint Contrastive Learning with Infinite Possibilities

Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei

Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes

Juan Luis GonzalezBello, Munchurl Kim

High-recall causal discovery for autocorrelated time series with latent confounders

Andreas Gerhardus, Jakob Runge

Efficient estimation of neural tuning during naturalistic behavior

Edoardo Balzani, Kaushik Lakshminarasimhan, Dora Angelaki, Cristina Savin

CSER: Communication-efficient SGD with Error Reset

Cong Xie, Shuai Zheng, Sanmi Koyejo, Indranil Gupta, Mu Li

How many samples is a good initial point worth in Low-rank Matrix Recovery?

Jialun Zhang, Richard Zhang

Generative Neurosymbolic Machines

Jindong Jiang, Sungjin Ahn

Self-Supervised Graph Transformer on Large-Scale Molecular Data

Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei

Contrastive learning of global and local features for medical image segmentation with limited annotations

Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar Shah, Vincent Conitzer

On Convergence of Nearest Neighbor Classifiers over Feature Transformations

Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang

Dynamic Regret of Convex and Smooth Functions

Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou

Value-driven Hindsight Modelling

Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?

Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

William Moses, Valentin Churavy

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs

Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Alexei Baevski, Yuhao Zhou, Michael Auli, Abdel-rahman Mohamed, Abdelrahman Mohamed

Improved Techniques for Training Score-Based Generative Models

Yang Song, Stefano Ermon

Matérn Gaussian Processes on Riemannian Manifolds

Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (he/him)

Escaping Saddle-Point Faster under Interpolation-like Conditions

Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra

Off-Policy Imitation Learning from Observations

Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

Investigating Gender Bias in Language Models Using Causal Mediation Analysis

Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo

Temporal Variability in Implicit Online Learning

Nicolò Campolongo, Francesco Orabona

Universally Quantized Neural Compression

Eirikur Agustsson, Lucas Theis

The Wasserstein Proximal Gradient Algorithm

Adil SALIM, Anna Korba, Giulia Luise

Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space

Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal

Kernel Based Progressive Distillation for Adder Neural Networks

Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing XU

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham

Locally Differentially Private (Contextual) Bandits Learning

Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang

Learning Black-Box Attackers with Transferable Priors and Query Feedback

Jiancheng YANG, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling

Tong Che, Ruixiang ZHANG, Jascha Sohl-dickstein, Hugo Larochelle, Liam Paull

Causal Imitation Learning With Unobserved Confounders

Junzhe Zhang, Daniel Kumor, Elias Bareinboim

Minimax Estimation of Conditional Moment Models

Nishanth Dikkala, Greg Lewis, Lester W. Mackey, Vasilis Syrgkanis

Few-Cost Salient Object Detection with Adversarial-Paced Learning

Dingwen Zhang, HaiBin Tian, Jungong Han

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Minh Vu, My T. Thai

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method

Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Benjamin Aubin, Florent Krzakala, Yue Lu, Lenka Zdeborová

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses

Kaivalya Rawal, Himabindu Lakkaraju

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos

Safe Reinforcement Learning via Curriculum Induction

Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal

Improving Neural Network Training in Low Dimensional Random Bases

Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li

Deeply Learned Spectral Total Variation Decomposition

Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola Schoenlieb

Training Generative Adversarial Networks with Limited Data

Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen

Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation

Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang

MetaPoison: Practical General-purpose Clean-label Data Poisoning

W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

A Bandit Learning Algorithm and Applications to Auction Design

Kim Thang Nguyen

Neural Architecture Generator Optimization

Robin Ru, Pedro Esperança, Fabio Maria Carlucci

Neural Topographic Factor Analysis for fMRI Data

Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J Benjamin Hutchinson, Ajay Satpute

TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation

DONGXU LI, Chenchen Xu, Xin Yu, Kaihao Zhang, Benjamin Swift

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks

Wenrui Zhang, Peng Li

Guiding Deep Molecular Optimization with Genetic Exploration

Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin

Rethinking Importance Weighting for Deep Learning under Distribution Shift

Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama

Implicit Graph Neural Networks

Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui

On the Trade-off between Adversarial and Backdoor Robustness

Cheng-Hsin Weng, Yan-Ting Lee, Shan-Hung (Brandon) Wu

Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology

Quynh N Nguyen, Marco Mondelli

A Novel Approach for Constrained Optimization in Graphical Models

Sara Rouhani, Tahrima Rahman, Vibhav Gogate

Counterexample-Guided Learning of Monotonic Neural Networks

Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning

Bhavya Kailkhura, Jayaraman Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou

High-Fidelity Generative Image Compression

Fabian Mentzer, George D Toderici, Michael Tschannen, Eirikur Agustsson