NeurIPS 2019

1,428 papers

Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions

Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas

A Meta-Analysis of Overfitting in Machine Learning

Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt

STREETS: A Novel Camera Network Dataset for Traffic Flow

Corey Snyder, Minh Do

Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

Muhammad Osama, Dave Zachariah, Peter Stoica

Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu

Statistical Model Aggregation via Parameter Matching

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang

A Little Is Enough: Circumventing Defenses For Distributed Learning

Gilad Baruch, Moran Baruch, Yoav Goldberg

Learning to Optimize in Swarms

Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen

Adversarial Music: Real world Audio Adversary against Wake-word Detection System

Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter

PRNet: Self-Supervised Learning for Partial-to-Partial Registration

Yue Wang, Justin M Solomon

Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning

Harm Van Seijen, Mehdi Fatemi, Arash Tavakoli

Face Reconstruction from Voice using Generative Adversarial Networks

Yandong Wen, Bhiksha Raj, Rita Singh

The Randomized Midpoint Method for Log-Concave Sampling

Ruoqi Shen, Yin Tat Lee

PIDForest: Anomaly Detection via Partial Identification

Parikshit Gopalan, Vatsal Sharan, Udi Wieder

Transfusion: Understanding Transfer Learning for Medical Imaging

Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, Samy Bengio

A Primal-Dual link between GANs and Autoencoders

Hisham Husain, Richard Nock, Robert C. Williamson

Universal Invariant and Equivariant Graph Neural Networks

Nicolas Keriven, Gabriel Peyré

Graph Structured Prediction Energy Networks

Colin Graber, Alexander Schwing

Double Quantization for Communication-Efficient Distributed Optimization

Yue Yu, Jiaxiang Wu, Longbo Huang

Meta Architecture Search

Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Sulaiman Alghunaim, Kun Yuan, Ali H. Sayed

DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

Matthieu SIMEONI, Sepand Kashani, Paul Hurley, Martin Vetterli

Weight Agnostic Neural Networks

Adam Gaier, David Ha

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende

Deep Model Transferability from Attribution Maps

Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song

Envy-Free Classification

Maria-Florina F Balcan, Travis Dick, Ritesh Noothigattu, Ariel D Procaccia

Efficient Convex Relaxations for Streaming PCA

Raman Arora, Teodor Vanislavov Marinov

Outlier Detection and Robust PCA Using a Convex Measure of Innovation

Mostafa Rahmani, Ping Li

Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth Spelke

Incremental Few-Shot Learning with Attention Attractor Networks

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel

Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples

Tengyu Xu, Shaofeng Zou, Yingbin Liang

Towards Hardware-Aware Tractable Learning of Probabilistic Models

Laura I Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck

Copula-like Variational Inference

Marcel Hirt, Petros Dellaportas, Alain Durmus

Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-yu Lin, Yung-Yu Chuang

Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update

Choi Sungik, Sae-Young Chung, Suyoung Lee, Su Young Lee

Selecting causal brain features with a single conditional independence test per feature

Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing

Efficient Deep Approximation of GMMs

Shirin Jalali, Carl Nuzman, Iraj Saniee

Constraint-based Causal Structure Learning with Consistent Separating Sets

Honghao Li, Vincent Cabeli, Nadir Sella, Herve Isambert

Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback

Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga

Thompson Sampling with Information Relaxation Penalties

Seungki Min, Costis Maglaras, Ciamac C. Moallemi

Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting

Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou

Conditional Structure Generation through Graph Variational Generative Adversarial Nets

Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li

Gradient based sample selection for online continual learning

Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama

Code Generation as a Dual Task of Code Summarization

Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin

How to Initialize your Network? Robust Initialization for WeightNorm & ResNets

Devansh Arpit, Víctor Campos, Yoshua Bengio

On the Curved Geometry of Accelerated Optimization

Aaron Defazio

Predicting the Politics of an Image Using Webly Supervised Data

Christopher Thomas, Adriana Kovashka

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim

A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

Xueying Bai, Jian Guan, Hongning Wang

Self-Routing Capsule Networks

Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim

On the number of variables to use in principal component regression

Ji Xu, Daniel J. Hsu

On Differentially Private Graph Sparsification and Applications

Raman Arora, Jalaj Upadhyay

Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits

Sivan Sabato

Sampled Softmax with Random Fourier Features

Ankit Singh Rawat, Jiecao Chen, Felix Xinnan X Yu, Ananda Theertha Suresh, Sanjiv Kumar

Hierarchical Optimal Transport for Multimodal Distribution Alignment

John Lee, Max Dabagia, Eva Dyer, Christopher Rozell

Pseudo-Extended Markov chain Monte Carlo

Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman

Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

Harsh Gupta, R. Srikant, Lei Ying

Rates of Convergence for Large-scale Nearest Neighbor Classification

Xingye Qiao, Jiexin Duan, Guang Cheng

Surfing: Iterative Optimization Over Incrementally Trained Deep Networks

Ganlin Song, Zhou Fan, John Lafferty

Certifiable Robustness to Graph Perturbations

Aleksandar Bojchevski, Stephan Günnemann

Graph-based Discriminators: Sample Complexity and Expressiveness

Roi Livni, Yishay Mansour

Fast structure learning with modular regularization

Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan

Untangling in Invariant Speech Recognition

Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz Elibol, Hanlin Tang

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann

Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory

Bin Hu, Usman Syed

Adversarial Examples Are Not Bugs, They Are Features

Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen

Margin-Based Generalization Lower Bounds for Boosted Classifiers

Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson

Using Statistics to Automate Stochastic Optimization

Hunter Lang, Lin Xiao, Pengchuan Zhang

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva

Online-Within-Online Meta-Learning

Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil

A New Perspective on Pool-Based Active Classification and False-Discovery Control

Lalit Jain, Kevin G. Jamieson

A Flexible Generative Framework for Graph-based Semi-supervised Learning

Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei

Disentangled behavioural representations

Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan

Discrete Flows: Invertible Generative Models of Discrete Data

Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

Structure Learning with Side Information: Sample Complexity

Saurabh Sihag, Ali Tajer

Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design

Faidra Georgia Monachou, Itai Ashlagi

Universality in Learning from Linear Measurements

Ehsan Abbasi, Fariborz Salehi, Babak Hassibi

Inherent Weight Normalization in Stochastic Neural Networks

Georgios Detorakis, Sourav Dutta, Abhishek Khanna, Matthew Jerry, Suman Datta

Modeling Conceptual Understanding in Image Reference Games

Rodolfo Corona Rodriguez, Stephan Alaniz, Zeynep Akata

Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up

Dominic Richards, Patrick Rebeschini

Cost Effective Active Search

Shali Jiang, Roman Garnett, Benjamin Moseley

Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD

PHUONG_HA NGUYEN, Lam Nguyen, Marten van Dijk

A neurally plausible model learns successor representations in partially observable environments

Eszter Vértes, Maneesh Sahani

Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs

Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

Learning Representations by Maximizing Mutual Information Across Views

Philip Bachman, R Devon Hjelm, William Buchwalter

User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning

Dirk van der Hoeven

Recurrent Registration Neural Networks for Deformable Image Registration

Robin Sandkühler, Simon Andermatt, Grzegorz Bauman, Sylvia Nyilas, Christoph Jud

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components

Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann

A Tensorized Transformer for Language Modeling

Xindian Ma, Peng Zhang, SHUAI ZHANG, Nan Duan, Yuexian Hou

Conditional Independence Testing using Generative Adversarial Networks

Alexis Bellot, Mihaela van der Schaar

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence

Fengxiang He, Tongliang Liu, Dacheng Tao

Amortized Bethe Free Energy Minimization for Learning MRFs

Sam Wiseman, Yoon Kim

Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity

Chulhee Yun, Suvrit Sra, Ali Jadbabaie

On Exact Computation with an Infinitely Wide Neural Net

Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov

Spectral Modification of Graphs for Improved Spectral Clustering

Ioannis Koutis, Huong Le

Multi-source Domain Adaptation for Semantic Segmentation

Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu