NeurIPS 2020

1,898 papers

Generative View Synthesis: From Single-view Semantics to Novel-view Images

Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker

Learning Representations from Audio-Visual Spatial Alignment

Pedro Morgado, Yi Li, Nuno Nvasconcelos

Randomized tests for high-dimensional regression: A more efficient and powerful solution

Yue Li, Ilmun Kim, Yuting Wei

Unsupervised Learning of Object Landmarks via Self-Training Correspondence

Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Andrew G Wilson, Pavel Izmailov

Robust Meta-learning for Mixed Linear Regression with Small Batches

Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh

A Non-Asymptotic Analysis for Stein Variational Gradient Descent

Anna Korba, Adil SALIM, Michael Arbel, Giulia Luise, Arthur Gretton

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation

Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh, Ambareesh Revanur, Venkatesh Babu R

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

Andy Shih, Stefano Ermon

Privacy Amplification via Random Check-Ins

Borja Balle, Peter Kairouz, Brendan Mcmahan, Om Thakkar, Abhradeep Guha Thakurta

Efficient Low Rank Gaussian Variational Inference for Neural Networks

Marcin Tomczak, Siddharth Swaroop, Richard Turner

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation

Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang

Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice

Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, Brain Laboratory International, Ila Fiete

Variational Policy Gradient Method for Reinforcement Learning with General Utilities

Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang

AvE: Assistance via Empowerment

Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel

POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis

Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks

Yusuke Tashiro, Yang Song, Stefano Ermon

Hierarchical Quantized Autoencoders

Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty

Learning Structured Distributions From Untrusted Batches: Faster and Simpler

Sitan Chen, Jerry Li, Ankur Moitra

Higher-Order Certification For Randomized Smoothing

Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu

Unsupervised Learning of Dense Visual Representations

Pedro O O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron C. Courville

Fast Fourier Convolution

Lu Chi, Borui Jiang, Yadong Mu

Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning

Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec

Continual Deep Learning by Functional Regularisation of Memorable Past

Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner

Low Distortion Block-Resampling with Spatially Stochastic Networks

Sarah Hong, Martin Arjovsky, Darryl Barnhart, Ian Thompson

Probabilistic Time Series Forecasting with Shape and Temporal Diversity

Vincent LE GUEN, Nicolas Thome

Reward-rational (implicit) choice: A unifying formalism for reward learning

Hong Jun Jeon, Smitha Milli, Anca Dragan

PAC-Bayes Learning Bounds for Sample-Dependent Priors

Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri

Influence-Augmented Online Planning for Complex Environments

Jinke He, Miguel Suau de Castro, Frans Oliehoek

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar

Learning Differential Equations that are Easy to Solve

Jacob Kelly, Jesse Bettencourt, Matthew J Johnson, David K. Duvenaud

Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms

Tengyu Xu, Zhe Wang, Yingbin Liang

Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations

Sebastian Farquhar, Lewis Smith, Yarin Gal

Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards

Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi

SuperLoss: A Generic Loss for Robust Curriculum Learning

Thibault Castells, Philippe Weinzaepfel, Jerome Revaud

Heavy-tailed Representations, Text Polarity Classification & Data Augmentation

Hamid JALALZAI, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni

Semantic Visual Navigation by Watching YouTube Videos

Matthew Chang, Arjun Gupta, Saurabh Gupta

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Zihang Dai, Guokun Lai, Yiming Yang, Quoc V Le, Quoc V. Le

Efficient semidefinite-programming-based inference for binary and multi-class MRFs

Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter

Object Goal Navigation using Goal-Oriented Semantic Exploration

Devendra Singh Chaplot, Dhiraj Prakashchand Gandhi, Abhinav Gupta, Ruslan Salakhutdinov

Munchausen Reinforcement Learning

Nino Vieillard, Olivier Pietquin, Matthieu Geist

On the Error Resistance of Hinge-Loss Minimization

Kunal Talwar

How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods

Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani Srivastava

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Elise van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, Max Welling

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh

Balanced Meta-Softmax for Long-Tailed Visual Recognition

Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao

Learning Loss for Test-Time Augmentation

Ildoo Kim, Younghoon Kim, Sungwoong Kim

Counterfactual Predictions under Runtime Confounding

Amanda Coston, Edward Kennedy, Alexandra Chouldechova

Predictive inference is free with the jackknife+-after-bootstrap

Byol Kim, Chen Xu, Rina Barber

Sequential Bayesian Experimental Design with Variable Cost Structure

Sue Zheng, David Hayden, Jason Pacheco, John W. Fisher III

From Predictions to Decisions: Using Lookahead Regularization

Nir Rosenfeld, Anna Hilgard, Sai Srivatsa Ravindranath, David C. Parkes

Adaptive Reduced Rank Regression

Qiong Wu, Felix MF Wong, Yanhua Li, Zhenming Liu, Varun Kanade

Searching for Low-Bit Weights in Quantized Neural Networks

Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing XU, Chao Xu

AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva

Fairness with Overlapping Groups; a Probabilistic Perspective

Forest Yang, Mouhamadou Cisse, Sanmi Koyejo

On the Power of Louvain in the Stochastic Block Model

Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

Differentially Private Clustering: Tight Approximation Ratios

Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine

JIAJIN LI, Caihua Chen, Anthony Man-Cho So

Sufficient dimension reduction for classification using principal optimal transport direction

Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong

Self-Supervised Relational Reasoning for Representation Learning

Massimiliano Patacchiola, Amos J. Storkey

Rethinking Learnable Tree Filter for Generic Feature Transform

Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Xiangyu Zhang

Counterfactual Data Augmentation using Locally Factored Dynamics

Silviu Pitis, Elliot Creager, Animesh Garg

Teaching a GAN What Not to Learn

Siddarth Asokan, Chandra Seelamantula

Dissecting Neural ODEs

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

Learning discrete distributions with infinite support

Doron Cohen, Aryeh Kontorovich, Geoﬀrey Wolfer

Learning About Objects by Learning to Interact with Them

Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi

An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch

Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna

OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

Taewon Jeong, Heeyoung Kim

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

Dennis Wei, Tian Gao, Yue Yu

On Warm-Starting Neural Network Training

Jordan Ash, Ryan P. Adams

CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching

Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie

Adaptive Discretization for Model-Based Reinforcement Learning

Sean Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Yu

Unsupervised Sound Separation Using Mixture Invariant Training

Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron Weiss, Kevin Wilson

Rethinking Pre-training and Self-training

Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu

Submodular Meta-Learning

Arman Adibi, Aryan Mokhtari, Hamed Hassani

On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples

Richard Zhang

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks

Yihong Gu, Weizhong Zhang, Cong Fang, Jason Lee, Tong Zhang

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm

Adil SALIM, Peter Richtarik

All Word Embeddings from One Embedding

Sho Takase, Sosuke Kobayashi

Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier

Variance reduction for Random Coordinate Descent-Langevin Monte Carlo

ZHIYAN DING, Qin Li

Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals

Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow

Didrik Nielsen, Ole Winther

Distribution-free binary classification: prediction sets, confidence intervals and calibration

Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas

On ranking via sorting by estimated expected utility

Clément Calauzènes, Nicolas Usunier

Deep Reinforcement and InfoMax Learning

Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman, R Devon Hjelm

Bidirectional Convolutional Poisson Gamma Dynamical Systems

wenchao chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang

Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts

Max Ryabinin, Anton Gusev

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization

Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

Natural Graph Networks

Pim de Haan, Taco S Cohen, Max Welling

Learning Disentangled Representations of Videos with Missing Data

Armand Comas, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu

Diverse Image Captioning with Context-Object Split Latent Spaces

Shweta Mahajan, Stefan Roth

Learning to Approximate a Bregman Divergence

Ali Siahkamari, XIDE XIA, Venkatesh Saligrama, David Castañón, Brian Kulis

Learning Global Transparent Models consistent with Local Contrastive Explanations

Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

Classification with Valid and Adaptive Coverage

Yaniv Romano, Matteo Sesia, Emmanuel Candes

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander Hauptmann

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar

Robust Correction of Sampling Bias using Cumulative Distribution Functions

Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck