NeurIPS 2019

1,428 papers

Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

Greg Yang

Combinatorial Bandits with Relative Feedback

Aadirupa Saha, Aditya Gopalan

Paraphrase Generation with Latent Bag of Words

Yao Fu, Yansong Feng, John P. Cunningham

Abstraction based Output Range Analysis for Neural Networks

Pavithra Prabhakar, Zahra Rahimi Afzal

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate

James Jordon, Jinsung Yoon, Mihaela van der Schaar

Stein Variational Gradient Descent With Matrix-Valued Kernels

Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu

Certainty Equivalence is Efficient for Linear Quadratic Control

Horia Mania, Stephen Tu, Benjamin Recht

Adversarial Training and Robustness for Multiple Perturbations

Florian Tramer, Dan Boneh

Copula Multi-label Learning

Weiwei Liu

Residual Flows for Invertible Generative Modeling

Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen

Implicit Regularization for Optimal Sparse Recovery

Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning

Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-jui Hsieh

Large Scale Markov Decision Processes with Changing Rewards

Adrian Rivera Cardoso, He Wang, Huan Xu

Transductive Zero-Shot Learning with Visual Structure Constraint

Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang

Minimal Variance Sampling in Stochastic Gradient Boosting

Bulat Ibragimov, Gleb Gusev

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation

zengfeng Huang, Ziyue Huang, Yilei WANG, Ke Yi

Variational Bayesian Decision-making for Continuous Utilities

Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami

Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

Creighton Heaukulani, Mark van der Wilk

A Game Theoretic Approach to Class-wise Selective Rationalization

Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola

Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem

Gonzalo Mena, Jonathan Niles-Weed

The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies

Basri Ronen, David Jacobs, Yoni Kasten, Shira Kritchman

Imitation-Projected Programmatic Reinforcement Learning

Abhinav Verma, Hoang Le, Yisong Yue, Swarat Chaudhuri

Explicitly disentangling image content from translation and rotation with spatial-VAE

Tristan Bepler, Ellen Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

Miika Aittala, Prafull Sharma, Lukas Murmann, Adam Yedidia, Gregory Wornell

Multi-mapping Image-to-Image Translation via Learning Disentanglement

Xiaoming Yu, Yuanqi Chen, Shan Liu, Thomas Li, Ge Li

Learning about an exponential amount of conditional distributions

Mohamed Belghazi, Maxime Oquab, David Lopez-paz

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning

Nathan Kallus, Masatoshi Uehara

Neural Jump Stochastic Differential Equations

Junteng Jia, Austin R Benson

Global Guarantees for Blind Demodulation with Generative Priors

Paul Hand, Babhru Joshi

Knowledge Extraction with No Observable Data

Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang

Submodular Function Minimization with Noisy Evaluation Oracle

Shinji Ito

Focused Quantization for Sparse CNNs

Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu

Reducing Noise in GAN Training with Variance Reduced Extragradient

Tatjana Chavdarova, Gauthier Gidel, Francois Fleuret, Simon Lacoste-Julien

Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration

Clarice Poon, Jingwei Liang

What Can ResNet Learn Efficiently, Going Beyond Kernels?

Zeyuan Allen-Zhu, Yuanzhi Li

VIREL: A Variational Inference Framework for Reinforcement Learning

Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson

Exploration via Hindsight Goal Generation

Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng

Meta-Curvature

Eunbyung Park, Junier B Oliva

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel

ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin

Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay

Frederic Koehler

Importance Weighted Hierarchical Variational Inference

Artem Sobolev, Dmitry P Vetrov

Point-Voxel CNN for Efficient 3D Deep Learning

Zhijian Liu, Haotian Tang, Yujun Lin, Song Han

On the Calibration of Multiclass Classification with Rejection

Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama

Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration

Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang

Learning in Generalized Linear Contextual Bandits with Stochastic Delays

Zhengyuan Zhou, Renyuan Xu, Jose Blanchet

Competitive Gradient Descent

Florian Schaefer, Anima Anandkumar

Non-Cooperative Inverse Reinforcement Learning

Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar

SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

Qian Lou, Lei Jiang

Demystifying Black-box Models with Symbolic Metamodels

Ahmed M. Alaa, Mihaela van der Schaar

Mutually Regressive Point Processes

Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski

Deep Structured Prediction for Facial Landmark Detection

Lisha Chen, Hui Su, Qiang Ji

Disentangling Influence: Using disentangled representations to audit model predictions

Charles Marx, Richard Phillips, Sorelle Friedler, Carlos Scheidegger, Suresh Venkatasubramanian

Streaming Bayesian Inference for Crowdsourced Classification

Edoardo Manino, Long Tran-Thanh, Nicholas Jennings

Discriminative Topic Modeling with Logistic LDA

Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis

Real-Time Reinforcement Learning

Simon Ramstedt, Chris Pal

On the Power and Limitations of Random Features for Understanding Neural Networks

Gilad Yehudai, Ohad Shamir

Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

Kaiqing Zhang, Zhuoran Yang, Tamer Basar

DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision

Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen

Bayesian Learning of Sum-Product Networks

Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani

Total Least Squares Regression in Input Sparsity Time

Huaian Diao, Zhao Song, David Woodruff, Xin Yang

Natalia Neverova, David Novotny, Andrea Vedaldi

Unsupervised Scalable Representation Learning for Multivariate Time Series

Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi

Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration

Robert Kleinberg, Kevin Leyton-brown, Brendan Lucier, Devon Graham

Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes

Gunpil Hwang, Seohyeon Kim, Hyeon-Min Bae

Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin

Learning Reward Machines for Partially Observable Reinforcement Learning

Rodrigo Toro Icarte, Ethan Waldie, Toryn Klassen, Rick Valenzano, Margarita Castro

Learning Sample-Specific Models with Low-Rank Personalized Regression

Ben Lengerich, Bryon Aragam, Eric P. Xing, Eric P Xing

Learning to Correlate in Multi-Player General-Sum Sequential Games

Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti

Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning

Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen

RUBi: Reducing Unimodal Biases for Visual Question Answering

Remi Cadene, Corentin Dancette, Hedi Ben younes, Matthieu Cord, Devi Parikh

Cold Case: The Lost MNIST Digits

Chhavi Yadav, Léon Bottou

MarginGAN: Adversarial Training in Semi-Supervised Learning

Jinhao Dong, Tong Lin

Flexible information routing in neural populations through stochastic comodulation

Caroline Haimerl, Cristina Savin, Eero Simoncelli

High-Dimensional Optimization in Adaptive Random Subspaces

Jonathan Lacotte, Mert Pilanci, Marco Pavone

Mixtape: Breaking the Softmax Bottleneck Efficiently

Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V Le

SpiderBoost and Momentum: Faster Variance Reduction Algorithms

Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh

Multiagent Evaluation under Incomplete Information

Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko

Conformalized Quantile Regression

Yaniv Romano, Evan Patterson, Emmanuel Candes

Write, Execute, Assess: Program Synthesis with a REPL

Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum

NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue

Dynamic Local Regret for Non-convex Online Forecasting

Sergul Aydore, Tianhao Zhu, Dean P. Foster

Planning in entropy-regularized Markov decision processes and games

Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Menard, Rémi Munos, Michal Valko

Understanding Sparse JL for Feature Hashing

Meena Jagadeesan

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling

Tengyang Xie, Yifei Ma, Yu-Xiang Wang

Parameter elimination in particle Gibbs sampling

Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten

Budgeted Reinforcement Learning in Continuous State Space

Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-ambrym Maillard

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellström, Cheng Zhang

Sparse High-Dimensional Isotonic Regression

David Gamarnik, Julia Gaudio

Learning from Label Proportions with Generative Adversarial Networks

Jiabin Liu, Bo Wang, Zhiquan Qi, YingJie Tian, Yong Shi

Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea

Safe Exploration for Interactive Machine Learning

Matteo Turchetta, Felix Berkenkamp, Andreas Krause

DAC: The Double Actor-Critic Architecture for Learning Options

Shangtong Zhang, Shimon Whiteson

Understanding the Role of Momentum in Stochastic Gradient Methods

Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao

AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters

XIA XIAO, Zigeng Wang, Sanguthevar Rajasekaran

Online Prediction of Switching Graph Labelings with Cluster Specialists

Mark Herbster, James Robinson

Twin Auxilary Classifiers GAN

Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

Jun Sun, Tianyi Chen, Georgios Giannakis, Zaiyue Yang

Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm

Amir-massoud Farahmand