NeurIPS 2019

1,428 papers

Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Rajat Sen, Hsiang-Fu Yu, Inderjit S Dhillon

Deep Supervised Summarization: Algorithm and Application to Learning Instructions

Chengguang Xu, Ehsan Elhamifar

Improving Textual Network Learning with Variational Homophilic Embeddings

Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen

Splitting Steepest Descent for Growing Neural Architectures

Lemeng Wu, Dilin Wang, Qiang Liu

Fully Neural Network based Model for General Temporal Point Processes

Takahiro Omi, Naonori Ueda, Kazuyuki Aihara

Function-Space Distributions over Kernels

Gregory Benton, Wesley J Maddox, Jayson Salkey, Julio Albinati, Andrew Gordon Wilson

Equipping Experts/Bandits with Long-term Memory

Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang

Universal Approximation of Input-Output Maps by Temporal Convolutional Nets

Joshua Hanson, Maxim Raginsky

Personalizing Many Decisions with High-Dimensional Covariates

Nima Hamidi, Mohsen Bayati, Kapil Gupta

DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, Ulrich Neumann

Exponentially convergent stochastic k-PCA without variance reduction

Cheng Tang

A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off

Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry

Policy Continuation with Hindsight Inverse Dynamics

Hao Sun, Zhizhong Li, Xiaotong Liu, Bolei Zhou, Dahua Lin

Secretary Ranking with Minimal Inversions

Sepehr Assadi, Eric Balkanski, Renato Leme

Singleshot : a scalable Tucker tensor decomposition

Abraham Traore, Maxime Berar, Alain Rakotomamonjy

Recurrent Space-time Graph Neural Networks

Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu

Evaluating Protein Transfer Learning with TAPE

Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen

Implicit Generation and Modeling with Energy Based Models

Yilun Du, Igor Mordatch

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko

Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers

Liwei Wu, Shuqing Li, Cho-jui Hsieh, James L Sharpnack

Unlocking Fairness: a Trade-off Revisited

Michael Wick, swetasudha panda, Jean-Baptiste Tristan

Few-shot Video-to-Video Synthesis

Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro

Mining GOLD Samples for Conditional GANs

Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin

Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

Hugo Caselles-Dupré, Michael Garcia Ortiz, David Filliat

Thompson Sampling for Multinomial Logit Contextual Bandits

Min-hwan Oh, Garud Iyengar

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak

Theoretical evidence for adversarial robustness through randomization

Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger

Semi-Parametric Dynamic Contextual Pricing

Virag Shah, Ramesh Johari, Jose Blanchet

Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees

Ruqi Zhang, Christopher M De Sa

Distribution-Independent PAC Learning of Halfspaces with Massart Noise

Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin

Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

Vincent Chen, Sen Wu, Alexander J Ratner, Jen Weng, Christopher Re

Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes

James Bailey, Georgios Piliouras

Explicit Disentanglement of Appearance and Perspective in Generative Models

Nicki Skafte, Søren Hauberg

Convergence of Adversarial Training in Overparametrized Neural Networks

Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-jui Hsieh, Liwei Wang

GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao

Globally optimal score-based learning of directed acyclic graphs in high-dimensions

Bryon Aragam, Arash Amini, Qing Zhou

Debiased Bayesian inference for average treatment effects

Kolyan Ray, Botond Szabo

Stand-Alone Self-Attention in Vision Models

Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya

Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer

Arsenii Vanunts, Alexey Drutsa

Self-Critical Reasoning for Robust Visual Question Answering

Jialin Wu, Raymond Mooney

Certified Adversarial Robustness with Additive Noise

Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu

iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang

The Implicit Bias of AdaGrad on Separable Data

Qian Qian, Xiaoyuan Qian

Optimal Stochastic and Online Learning with Individual Iterates

Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou

Unlabeled Data Improves Adversarial Robustness

Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang

Unsupervised Object Segmentation by Redrawing

Mickaël Chen, Thierry Artières, Ludovic DENOYER

REM: From Structural Entropy to Community Structure Deception

Yiwei Liu, Jiamou Liu, Zijian Zhang, Liehuang Zhu, Angsheng Li

Improving Black-box Adversarial Attacks with a Transfer-based Prior

Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu

Anti-efficient encoding in emergent communication

Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni

Scalable inference of topic evolution via models for latent geometric structures

Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, Xuanlong Nguyen

First Order Motion Model for Image Animation

Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe

R2D2: Reliable and Repeatable Detector and Descriptor

Jerome Revaud, Cesar De Souza, Martin Humenberger, Philippe Weinzaepfel

Policy Poisoning in Batch Reinforcement Learning and Control

Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu

Practical and Consistent Estimation of f-Divergences

Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O Tolstikhin

Exact inference in structured prediction

Kevin Bello, Jean Honorio

Precision-Recall Balanced Topic Modelling

Seppo Virtanen, Mark Girolami

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon

A coupled autoencoder approach for multi-modal analysis of cell types

Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn

Learning to Screen

Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran

Backprop with Approximate Activations for Memory-efficient Network Training

Ayan Chakrabarti, Benjamin Moseley

Balancing Efficiency and Fairness in On-Demand Ridesourcing

Nixie S Lesmana, Xuan Zhang, Xiaohui Bei

Hypothesis Set Stability and Generalization

Dylan J Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri

Generative Modeling by Estimating Gradients of the Data Distribution

Yang Song, Stefano Ermon

Understanding and Improving Layer Normalization

Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

The spiked matrix model with generative priors

Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

Shengchao Liu, Mehmet F Demirel, Yingyu Liang

Adaptive Sequence Submodularity

Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi

Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems

Young Hun Jung, Ambuj Tewari

Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer

Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang

Practical Two-Step Lookahead Bayesian Optimization

Jian Wu, Peter Frazier

Online sampling from log-concave distributions

Holden Lee, Oren Mangoubi, Nisheeth Vishnoi

Progressive Augmentation of GANs

Dan Zhang, Anna Khoreva

Stacked Capsule Autoencoders

Adam Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton

Topology-Preserving Deep Image Segmentation

Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen

An Adaptive Empirical Bayesian Method for Sparse Deep Learning

Wei Deng, Xiao Zhang, Faming Liang, Guang Lin

Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

Robert Ness, Kaushal Paneri, Olga Vitek

Tree-Sliced Variants of Wasserstein Distances

Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi

Sample Adaptive MCMC

Michael Zhu

Combinatorial Bayesian Optimization using the Graph Cartesian Product

Changyong Oh, Jakub Tomczak, Efstratios Gavves, Max Welling

Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks

Yiwen Guo, Ziang Yan, Changshui Zhang

Model Compression with Adversarial Robustness: A Unified Optimization Framework

Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang

Are Sixteen Heads Really Better than One?

Paul Michel, Omer Levy, Graham Neubig

Implicit Posterior Variational Inference for Deep Gaussian Processes

Haibin YU, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai

Uncertainty-based Continual Learning with Adaptive Regularization

Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon

Shaping Belief States with Generative Environment Models for RL

Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic

Interval timing in deep reinforcement learning agents

Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo

Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks

Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda

Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

Erik Nijkamp, Mitch Hill, Song-chun Zhu, Ying Nian Wu

SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

Seyed Kamyar Seyed Ghasemipour, Shixiang (Shane) Gu, Richard Zemel

Inducing brain-relevant bias in natural language processing models

Dan Schwartz, Mariya Toneva, Leila Wehbe

Convolution with even-sized kernels and symmetric padding

Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W Koh, Stefano Ermon

Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets

Daniel Kumor, Bryant Chen, Elias Bareinboim

Unconstrained Monotonic Neural Networks

Antoine Wehenkel, Gilles Louppe

Domain Generalization via Model-Agnostic Learning of Semantic Features

Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, Ben Glocker

Teaching Multiple Concepts to a Forgetful Learner

Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause