NeurIPS 2020

1,898 papers

Deep Inverse Q-learning with Constraints

Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network

Prune Truong, Martin Danelljan, Radu Timofte, Luc V Gool, Luc V. Gool

Transfer Learning via ℓ_1 Regularization

Masaaki Takada, Hironori Fujisawa

DISK: Learning local features with policy gradient

Michał Tyszkiewicz, Pascal Fua, Eduard Trulls

Rational neural networks

Nicolas Boulle, Yuji Nakatsukasa, Alex Townsend

Learning Utilities and Equilibria in Non-Truthful Auctions

Hu Fu, Tao LIN

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

Scott Fujimoto, David Meger, Doina Precup

GradAug: A New Regularization Method for Deep Neural Networks

Taojiannan Yang, Sijie Zhu, Chen Chen

3D Shape Reconstruction from Vision and Touch

Edward Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger

Mutual exclusivity as a challenge for deep neural networks

Kanishk Gandhi, Brenden M Lake

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Sebastian Curi, Felix Berkenkamp, Andreas Krause

Building powerful and equivariant graph neural networks with structural message-passing

Clément Vignac, Andreas Loukas, Pascal Frossard

MOPO: Model-based Offline Policy Optimization

Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou

Calibration of Shared Equilibria in General Sum Partially Observable Markov Games

Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela M. Veloso

Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms

Hilal Asi, John C. Duchi

Graph Cross Networks with Vertex Infomax Pooling

Maosen Li, Siheng Chen, Ya Zhang, Ivor W. Tsang

Neural FFTs for Universal Texture Image Synthesis

Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao

Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

Chaoyang He, Murali Annavaram, Salman Avestimehr

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

Sang-gil Lee, Sungwon Kim, Sungroh Yoon

Using noise to probe recurrent neural network structure and prune synapses

Eli Moore, Rishidev Chaudhuri

Learning Deep Attribution Priors Based On Prior Knowledge

Ethan Weinberger, Joseph Janizek, Su-in Lee

Online Planning with Lookahead Policies

Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Minjia Zhang, Yuxiong He

Smoothly Bounding User Contributions in Differential Privacy

Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

Directional Pruning of Deep Neural Networks

Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity

Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Self-Imitation Learning via Generalized Lower Bound Q-learning

Yunhao Tang

Learning sparse codes from compressed representations with biologically plausible local wiring constraints

Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell

A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent

Zhenyu Liao, Romain Couillet, Michael W. Mahoney

RANet: Region Attention Network for Semantic Segmentation

Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin

Constant-Expansion Suffices for Compressed Sensing with Generative Priors

Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations

Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency

Robert Geirhos, Kristof Meding, Felix A. Wichmann

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Marc Finzi, Ke Alexander Wang, Andrew G Wilson

Learning Bounds for Risk-sensitive Learning

Jaeho Lee, Sejun Park, Jinwoo Shin

Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces

Akash Saha, Balamurugan Palaniappan

Transferable Graph Optimizers for ML Compilers

Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

Mehdi Rezaee, Francis Ferraro

MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics

Luca De Gennaro Aquino, Stephan Eckstein

Equivariant Networks for Hierarchical Structures

Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

Identifying signal and noise structure in neural population activity with Gaussian process factor models

Stephen Keeley, Mikio Aoi, Yiyi Yu, Spencer Smith, Jonathan W Pillow

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers

Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi

Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Xiangyi Chen, Steven Z. Wu, Mingyi Hong

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis

Gang Wang, Songtao Lu, Georgios Giannakis, Gerald Tesauro, Jian Sun

Group-Fair Online Allocation in Continuous Time

Semih Cayci, Swati Gupta, Atilla Eryilmaz

Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech

Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S Turek

The Potts-Ising model for discrete multivariate data

Zahra Razaee, Arash Amini

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning

Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz

Deep Automodulators

Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations

Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions

Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi

A Local Temporal Difference Code for Distributional Reinforcement Learning

Pablo Tano, Peter Dayan, Alexandre Pouget

Multi-agent active perception with prediction rewards

Mikko Lauri, Frans Oliehoek

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms

Sascha Saralajew, Lars Holdijk, Thomas Villmann

Smoothed Geometry for Robust Attribution

Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson

Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Marwa El Halabi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakab Tardos, Jakub M Tarnawski

Neurosymbolic Transformers for Multi-Agent Communication

Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals

Ilias Diakonikolas, Daniel Kane, Nikos Zarifis

Efficient Learning of Discrete Graphical Models

Marc Vuffray, Sidhant Misra, Andrey Lokhov

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Cheng Chi, Fangyun Wei, Han Hu

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen

Evolving Normalization-Activation Layers

Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc V Le, Quoc V. Le

Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control

Giorgos ('Yorgos') Mamakoukas, Orest Xherija, Todd Murphey

Adversarial Counterfactual Learning and Evaluation for Recommender System

Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method

Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Ziqi Ke, Haris Vikalo

VarGrad: A Low-Variance Gradient Estimator for Variational Inference

Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco Ruiz, Omer Deniz Akyildiz

Linear Time Sinkhorn Divergences using Positive Features

meyer scetbon, Marco Cuturi

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová

Is normalization indispensable for training deep neural network?

Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj

Latent Bandits Revisited

Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed

Group Contextual Encoding for 3D Point Clouds

Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning

Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun

Robust Disentanglement of a Few Factors at a Time using rPU-VAE

Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik

Instance-wise Feature Grouping

Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks

Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang

The interplay between randomness and structure during learning in RNNs

Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, Omri Barak

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge

Sample Complexity of Uniform Convergence for Multicalibration

Eliran Shabat, Lee Cohen, Yishay Mansour

Learning compositional functions via multiplicative weight updates

Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar

Video Frame Interpolation without Temporal Priors

Youjian Zhang, Chaoyue Wang, Dacheng Tao

Linear Disentangled Representations and Unsupervised Action Estimation

Matthew Painter, Adam Prugel-Bennett, Jonathon Hare

Instance Selection for GANs

Terrance DeVries, Michal Drozdzal, Graham W. Taylor

Reliable Graph Neural Networks via Robust Aggregation

Simon Geisler, Daniel Zügner, Stephan Günnemann

Principal Neighbourhood Aggregation for Graph Nets

Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Lió, Petar Veličković

Program Synthesis with Pragmatic Communication

Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama

Interferobot: aligning an optical interferometer by a reinforcement learning agent

Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky

On Infinite-Width Hypernetworks

Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang

Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction

Benoît Colange, Jaakko Peltonen, Michael Aupetit, Denys Dutykh, Sylvain Lespinats

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications

Sarah Perrin, Julien Pérolat, Mathieu Lauriere, Matthieu Geist, Romuald Elie

Node Embeddings and Exact Low-Rank Representations of Complex Networks

Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps

Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai

Exemplar Guided Active Learning

Jason S Hartford, Kevin Leyton-brown, Hadas Raviv, Dan Padnos, Shahar Lev

POMDPs in Continuous Time and Discrete Spaces

Bastian Alt, Matthias Schultheis, Heinz Koeppl

Language-Conditioned Imitation Learning for Robot Manipulation Tasks

Simon Stepputtis, Joseph P. Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral

Self-Learning Transformations for Improving Gaze and Head Redirection

Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning

Iro Laina, Ruth Fong, Andrea Vedaldi

Deep Energy-based Modeling of Discrete-Time Physics

Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi