NeurIPS 2019

1,428 papers

Tensor Monte Carlo: Particle Methods for the GPU era

Laurence Aitchison

Distributed Low-rank Matrix Factorization With Exact Consensus

Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B Wakin

Locally Private Gaussian Estimation

Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Steven Z. Wu

Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation

Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, anderson nascimento

Semi-flat minima and saddle points by embedding neural networks to overparameterization

Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection

Yihe Dong, Samuel Hopkins, Jerry Li

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Niklas Gebauer, Michael Gastegger, Kristof Schütt

Learning Distributions Generated by One-Layer ReLU Networks

Shanshan Wu, Alexandros G Dimakis, Sujay Sanghavi

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

A Family of Robust Stochastic Operators for Reinforcement Learning

Yingdong Lu, Mark Squillante, Chai Wah Wu

Multi-objects Generation with Amortized Structural Regularization

Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing

Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su

Better Exploration with Optimistic Actor Critic

Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann

Scalable Deep Generative Relational Model with High-Order Node Dependence

Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen

Random Projections with Asymmetric Quantization

Xiaoyun Li, Ping Li

Space and Time Efficient Kernel Density Estimation in High Dimensions

Arturs Backurs, Piotr Indyk, Tal Wagner

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song

Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen

Learning Disentangled Representations for Recommendation

Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu

Fast and Accurate Stochastic Gradient Estimation

Beidi Chen, Yingchen Xu, Anshumali Shrivastava

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Michael Rabbat

Nonconvex Low-Rank Tensor Completion from Noisy Data

Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle

Accurate Layerwise Interpretable Competence Estimation

Vickram Rajendran, William LeVine

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe

On the Expressive Power of Deep Polynomial Neural Networks

Joe Kileel, Matthew Trager, Joan Bruna

Selective Sampling-based Scalable Sparse Subspace Clustering

Shin Matsushima, Maria Brbic

Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function

Aviv Rosenberg, Yishay Mansour

Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives

Wang Chi Cheung

Network Pruning via Transformable Architecture Search

Xuanyi Dong, Yi Yang

Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations

Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun

Finite-Sample Analysis for SARSA with Linear Function Approximation

Shaofeng Zou, Tengyu Xu, Yingbin Liang

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto

Reconciling λ-Returns with Experience Replay

Brett Daley, Christopher Amato

Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization

Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman

Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling

Ping Li, Xiaoyun Li, Cun-hui Zhang

The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks

Ryo Karakida, Shotaro Akaho, Shun-ichi Amari

An Embedding Framework for Consistent Polyhedral Surrogates

Jessica Finocchiaro, Rafael Frongillo, Bo Waggoner

Generative Well-intentioned Networks

Justin Cosentino, Jun Zhu

Integer Discrete Flows and Lossless Compression

Emiel Hoogeboom, Jorn Peters, Rianne van den Berg, Max Welling

Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function

Zihan Zhang, Xiangyang Ji

Policy Learning for Fairness in Ranking

Ashudeep Singh, Thorsten Joachims

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka

Computing Full Conformal Prediction Set with Approximate Homotopy

Eugene Ndiaye, Ichiro Takeuchi

Large Memory Layers with Product Keys

Guillaume Lample, Alexandre Sablayrolles, Marc'aurelio Ranzato, Ludovic DENOYER, Herve Jegou

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov

Graph Transformer Networks

Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J Kim

Partially Encrypted Deep Learning using Functional Encryption

Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S Turek, Tim Mattson

Differentiable Convex Optimization Layers

Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond

Continuous-time Models for Stochastic Optimization Algorithms

Antonio Orvieto, Aurelien Lucchi

Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes

Siqi Liu, Milos Hauskrecht

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization

Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng

Input-Output Equivalence of Unitary and Contractive RNNs

Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher

Variational Structured Semantic Inference for Diverse Image Captioning

Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang

Bridging Machine Learning and Logical Reasoning by Abductive Learning

Wang-Zhou Dai, Qiuling Xu, Yang Yu, Zhi-Hua Zhou

Multiway clustering via tensor block models

Miaoyan Wang, Yuchen Zeng

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther

AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

Jiong Zhang, Hsiang-Fu Yu, Inderjit S Dhillon

Exact Rate-Distortion in Autoencoders via Echo Noise

Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg

Regret Bounds for Learning State Representations in Reinforcement Learning

Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-ambrym Maillard

MaxGap Bandit: Adaptive Algorithms for Approximate Ranking

Sumeet Katariya, Ardhendu Tripathy, Robert Nowak

A Graph Theoretic Additive Approximation of Optimal Transport

Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra

A Condition Number for Joint Optimization of Cycle-Consistent Networks

Leonidas Guibas, Qixing Huang, Zhenxiao Liang

Importance Resampling for Off-policy Prediction

Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White

Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection

Pan Li, I Chien, Olgica Milenkovic

Limits of Private Learning with Access to Public Data

Noga Alon, Raef Bassily, Shay Moran

A neurally plausible model for online recognition and postdiction in a dynamical environment

Li Kevin Wenliang, Maneesh Sahani

Recovering Bandits

Ciara Pike-Burke, Steffen Grünewälder

Thresholding Bandit with Optimal Aggregate Regret

Chao Tao, Saúl Blanco, Jian Peng, Yuan Zhou

Cross-Domain Transferability of Adversarial Perturbations

Muhammad Muzammal Naseer, Salman H Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering

Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, ecprice, Alistair Stewart

Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

Jonathan Sauder, Bjarne Sievers

The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection

Vladimir V. Kniaz, Vladimir Knyaz, Fabio Remondino

Visual Concept-Metaconcept Learning

Chi Han, Jiayuan Mao, Chuang Gan, Josh Tenenbaum, Jiajun Wu

Learning Nearest Neighbor Graphs from Noisy Distance Samples

Blake Mason, Ardhendu Tripathy, Robert Nowak

Neural Temporal-Difference Learning Converges to Global Optima

Qi Cai, Zhuoran Yang, Jason Lee, Zhaoran Wang

Block Coordinate Regularization by Denoising

Yu Sun, Jiaming Liu, Ulugbek Kamilov

Object landmark discovery through unsupervised adaptation

Enrique Sanchez, Georgios Tzimiropoulos

ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang

Private Hypothesis Selection

Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu

Deep ReLU Networks Have Surprisingly Few Activation Patterns

Boris Hanin, David Rolnick

DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

Rixon Crane, Fred Roosta

Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes

Jun Yang, Shengyang Sun, Daniel M. Roy

Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang

Distributional Reward Decomposition for Reinforcement Learning

Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-yan Liu

Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

Rishidev Chaudhuri, Ila Fiete

Kernelized Bayesian Softmax for Text Generation

Ning Miao, Hao Zhou, Chengqi Zhao, Wenxian Shi, Lei Li

Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation

Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George Pappas

Learning Compositional Neural Programs with Recursive Tree Search and Planning

Thomas PIERROT, Guillaume Ligner, Scott E Reed, Olivier Sigaud, Nicolas Perrin

Learning Deep Bilinear Transformation for Fine-grained Image Representation

Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo

Adaptive Density Estimation for Generative Models

Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek

Screening Sinkhorn Algorithm for Regularized Optimal Transport

Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy

Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy

Jonathan Ullman, Adam Sealfon

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

Pedro Mercado, Francesco Tudisco, Matthias Hein

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

Andreas Kirsch, Joost van Amersfoort, Yarin Gal

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

Aaron Voelker, Ivana Kajić, Chris Eliasmith

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine