NeurIPS 2019

1,428 papers

Game Design for Eliciting Distinguishable Behavior

Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K Ravikumar

Hamiltonian descent for composite objectives

Brendan O'Donoghue, Chris J Maddison, Chris J. Maddison

A New Defense Against Adversarial Images: Turning a Weakness into a Strength

Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger

Making AI Forget You: Data Deletion in Machine Learning

Antonio Ginart, Melody Guan, Gregory Valiant, James Y Zou

Equitable Stable Matchings in Quadratic Time

Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras

Online Normalization for Training Neural Networks

Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Koster, Ryan Reece

Quantum Embedding of Knowledge for Reasoning

Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam

Learning Nonsymmetric Determinantal Point Processes

Mike Gartrell, Victor-Emmanuel Brunel, Elvis DOHMATOB, Syrine Krichene

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup

Sebastian Goldt, Madhu Advani, Andrew M Saxe, Florent Krzakala, Lenka Zdeborová

Time-series Generative Adversarial Networks

Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance

Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau

Iterative Least Trimmed Squares for Mixed Linear Regression

Yanyao Shen, Sujay Sanghavi

Superset Technique for Approximate Recovery in One-Bit Compressed Sensing

Larkin Flodin, Venkata Gandikota, Arya Mazumdar

Goal-conditioned Imitation Learning

Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp

Planning with Goal-Conditioned Policies

Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine

Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks

Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum

Efficiently Learning Fourier Sparse Set Functions

Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee

Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning

Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang

Transfer Learning via Minimizing the Performance Gap Between Domains

Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton

Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka

Implicit Regularization of Accelerated Methods in Hilbert Spaces

Nicolò Pagliana, Lorenzo Rosasco

A Necessary and Sufficient Stability Notion for Adaptive Generalization

Moshe Shenfeld, Katrina Ligett

Rethinking Kernel Methods for Node Representation Learning on Graphs

Yu Tian, Long Zhao, Xi Peng, Dimitris Metaxas

Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks

Hosein Hasani, Mahdieh Soleymani, Hamid Aghajan

On the Inductive Bias of Neural Tangent Kernels

Alberto Bietti, Julien Mairal

Ultra Fast Medoid Identification via Correlated Sequential Halving

Tavor Baharav, David Tse

Privacy Amplification by Mixing and Diffusion Mechanisms

Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek

Manipulating a Learning Defender and Ways to Counteract

Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael Wooldridge

Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning

Jian Ni, Shanghang Zhang, Haiyong Xie

Latent distance estimation for random geometric graphs

Ernesto Araya Valdivia, De Castro Yohann

A Step Toward Quantifying Independently Reproducible Machine Learning Research

Edward Raff

SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits

Etienne Boursier, Vianney Perchet

Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction

Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim

Online Learning via the Differential Privacy Lens

Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction

Difan Zou, Pan Xu, Quanquan Gu

Fisher Efficient Inference of Intractable Models

Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen

Alleviating Label Switching with Optimal Transport

Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M Solomon

An adaptive Mirror-Prox method for variational inequalities with singular operators

Kimon Antonakopoulos, Veronica Belmega, Panayotis Mertikopoulos

Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity

Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A Efros

Fine-grained Optimization of Deep Neural Networks

Mete Ozay

Metric Learning for Adversarial Robustness

Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray

Adaptive GNN for Image Analysis and Editing

Lingyu Liang, LianWen Jin, Yong Xu

Unified Language Model Pre-training for Natural Language Understanding and Generation

Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu

Learning by Abstraction: The Neural State Machine

Drew Hudson, Christopher D. Manning

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

Francisco Garcia, Philip S. Thomas

Constrained Reinforcement Learning Has Zero Duality Gap

Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro

Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models

Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski

Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization

Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe

Learning Auctions with Robust Incentive Guarantees

Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H Morgenstern

Limitations of Lazy Training of Two-layers Neural Network

Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

Necessary and Sufficient Geometries for Gradient Methods

Daniel Levy, John C. Duchi

Learning low-dimensional state embeddings and metastable clusters from time series data

Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang

Implicit Regularization in Deep Matrix Factorization

Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo

Post training 4-bit quantization of convolutional networks for rapid-deployment

Ron Banner, Yury Nahshan, Daniel Soudry

Efficient and Thrifty Voting by Any Means Necessary

Debmalya Mandal, Ariel D Procaccia, Nisarg Shah, David Woodruff

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E Turner, José Miguel Hernández-Lobato

Cross-lingual Language Model Pretraining

Alexis CONNEAU, Guillaume Lample

ETNet: Error Transition Network for Arbitrary Style Transfer

Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao

Stochastic Frank-Wolfe for Composite Convex Minimization

Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher

Learning to Self-Train for Semi-Supervised Few-Shot Classification

Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng

NAT: Neural Architecture Transformer for Accurate and Compact Architectures

Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen

Are Labels Required for Improving Adversarial Robustness?

Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth

On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons

Wenbo Ren, Jia (Kevin) Liu, Ness Shroff

Learning to Infer Implicit Surfaces without 3D Supervision

Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury

Multi-marginal Wasserstein GAN

Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen

LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition

Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang, Larry S. Davis

Provable Gradient Variance Guarantees for Black-Box Variational Inference

Justin Domke

An Algorithm to Learn Polytree Networks with Hidden Nodes

Firoozeh Sepehr, Donatello Materassi

Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

Yuanzhi Li, Colin Wei, Tengyu Ma

Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

Weishi Shi, Qi Yu

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms

Mahesh Chandra Mukkamala, Peter Ochs

RSN: Randomized Subspace Newton

Robert Gower, Dmitry Kovalev, Felix Lieder, Peter Richtarik

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

Attribution-Based Confidence Metric For Deep Neural Networks

Susmit Jha, Sunny Raj, Steven Fernandes, Sumit K Jha, Somesh Jha

Variance Reduction in Bipartite Experiments through Correlation Clustering

Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

Volumetric Correspondence Networks for Optical Flow

Gengshan Yang, Deva Ramanan

Learning Conditional Deformable Templates with Convolutional Networks

Adrian Dalca, Marianne Rakic, John Guttag, Mert Sabuncu

Paradoxes in Fair Machine Learning

Paul Goelz, Anson Kahng, Ariel D Procaccia

Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks

Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa, Hector Corrada Bravo, Soheil Feizi

Explanations can be manipulated and geometry is to blame

Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller

Kernel Stein Tests for Multiple Model Comparison

Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum

Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu

Provably Powerful Graph Networks

Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman

Minimum Stein Discrepancy Estimators

Alessandro Barp, François-Xavier Briol, Andrew Duncan, Mark Girolami, Lester Mackey

Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels

Michela Meister, Tamas Sarlos, David Woodruff

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li

A General Theory of Equivariant CNNs on Homogeneous Spaces

Taco S Cohen, Mario Geiger, Maurice Weiler

Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs

Marek Petrik, Reazul Hasan Russel

Machine Teaching of Active Sequential Learners

Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski

On the Value of Target Data in Transfer Learning

Steve Hanneke, Samory Kpotufe

Landmark Ordinal Embedding

Nikhil Ghosh, Yuxin Chen, Yisong Yue

PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points

Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu

Expressive power of tensor-network factorizations for probabilistic modeling

Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac