NeurIPS 2018

1,009 papers

Modern Neural Networks Generalize on Small Data Sets

Matthew Olson, Abraham Wyner, Richard Berk

Equality of Opportunity in Classification: A Causal Approach

Junzhe Zhang, Elias Bareinboim

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

Tianyi Chen, Georgios Giannakis, Tao Sun, Wotao Yin

Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

Abhinav Gupta, Adithyavairavan Murali, Dhiraj Prakashchand Gandhi, Lerrel Pinto

Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima

Yaodong Yu, Pan Xu, Quanquan Gu

Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced

Simon S Du, Wei Hu, Jason Lee

Dual Swap Disentangling

Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao

A Stein variational Newton method

Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl

Flexible neural representation for physics prediction

Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F Fei-Fei

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu

Practical exact algorithm for trembling-hand equilibrium refinements in games

Gabriele Farina, Nicola Gatti, Tuomas Sandholm

Non-Local Recurrent Network for Image Restoration

Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang

Sharp Bounds for Generalized Uniformity Testing

Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart

SEGA: Variance Reduction via Gradient Sketching

Filip Hanzely, Konstantin Mishchenko, Peter Richtarik

Loss Functions for Multiset Prediction

Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang

A Mathematical Model For Optimal Decisions In A Representative Democracy

Malik Magdon-Ismail, Lirong Xia

Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators

Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization

Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao

Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives

Song Zhou, Swati Gupta, Madeleine Udell

How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?

Richard Zhang, Cedric Josz, Somayeh Sojoudi, Javad Lavaei

A Practical Algorithm for Distributed Clustering and Outlier Detection

Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang

Fast deep reinforcement learning using online adjustments from the past

Steven Hansen, Alexander Pritzel, Pablo Sprechmann, Andre Barreto, Charles Blundell

Improved Network Robustness with Adversary Critic

Alexander Matyasko, Lap-Pui Chau

Assessing Generative Models via Precision and Recall

Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

Adversarially Robust Generalization Requires More Data

Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Mean-field theory of graph neural networks in graph partitioning

Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi

Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang

Manifold Structured Prediction

Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, Lorenzo Rosasco

Learning convex bounds for linear quadratic control policy synthesis

Jack Umenberger, Thomas B Schön

PointCNN: Convolution On X-Transformed Points

Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di

Robust Subspace Approximation in a Stream

Roie Levin, Anish Prasad Sevekari, David Woodruff

CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces

Liheng Zhang, Marzieh Edraki, Guo-Jun Qi

Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task

Dalin Guo, Angela J. Yu

LF-Net: Learning Local Features from Images

Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi

Adaptive Negative Curvature Descent with Applications in Non-convex Optimization

Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang

Context-dependent upper-confidence bounds for directed exploration

Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, Ni Lao

Communication Efficient Parallel Algorithms for Optimization on Manifolds

Bayan Saparbayeva, Michael Zhang, Lizhen Lin

Incorporating Context into Language Encoding Models for fMRI

Shailee Jain, Alexander Huth

BinGAN: Learning Compact Binary Descriptors with a Regularized GAN

Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski

Proximal SCOPE for Distributed Sparse Learning

Shenyi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-jun Li

BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training

Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang

An Information-Theoretic Analysis for Thompson Sampling with Many Actions

Shi Dong, Benjamin Van Roy

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing

Optimization over Continuous and Multi-dimensional Decisions with Observational Data

Dimitris Bertsimas, Christopher McCord

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting

Hippolyt Ritter, Aleksandar Botev, David Barber

Fast and Effective Robustness Certification

Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev

Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing

Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu

Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

Zhilu Zhang, Mert Sabuncu

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames

Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines

Proximal Graphical Event Models

Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao

Differentially Private Change-Point Detection

Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang

Learning latent variable structured prediction models with Gaussian perturbations

Kevin Bello, Jean Honorio

Neural Nearest Neighbors Networks

Tobias Plötz, Stefan Roth

Learning Versatile Filters for Efficient Convolutional Neural Networks

Yunhe Wang, Chang Xu, Chunjing XU, Chao Xu, Dacheng Tao

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Parsimonious Bayesian deep networks

Mingyuan Zhou

Embedding Logical Queries on Knowledge Graphs

Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec

Snap ML: A Hierarchical Framework for Machine Learning

Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel

Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks

Anirvan Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri Chklovskii

Video Prediction via Selective Sampling

Jingwei Xu, Bingbing Ni, Xiaokang Yang

HOUDINI: Lifelong Learning as Program Synthesis

Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

(Probably) Concave Graph Matching

Haggai Maron, Yaron Lipman

Tight Bounds for Collaborative PAC Learning via Multiplicative Weights

Jiecao Chen, Qin Zhang, Yuan Zhou

Learning long-range spatial dependencies with horizontal gated recurrent units

Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre

Efficient Stochastic Gradient Hard Thresholding

Pan Zhou, Xiaotong Yuan, Jiashi Feng

Dirichlet belief networks for topic structure learning

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou

Minimax Statistical Learning with Wasserstein distances

Jaeho Lee, Maxim Raginsky

Bandit Learning with Positive Externalities

Virag Shah, Jose Blanchet, Ramesh Johari

The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal

Jiantao Jiao, Weihao Gao, Yanjun Han

Convex Elicitation of Continuous Properties

Jessica Finocchiaro, Rafael Frongillo

A Reduction for Efficient LDA Topic Reconstruction

Matteo Almanza, Flavio Chierichetti, Alessandro Panconesi, Andrea Vattani

Step Size Matters in Deep Learning

Kamil Nar, Shankar Sastry

Solving Large Sequential Games with the Excessive Gap Technique

Christian Kroer, Gabriele Farina, Tuomas Sandholm

Scalable methods for 8-bit training of neural networks

Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry

Efficient Loss-Based Decoding on Graphs for Extreme Classification

Itay Evron, Edward Moroshko, Koby Crammer

The Effect of Network Width on the Performance of Large-batch Training

Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris Papailiopoulos, Paraschos Koutris

Removing the Feature Correlation Effect of Multiplicative Noise

Zijun Zhang, Yining Zhang, Zongpeng Li

Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks

Zhihao Zheng, Pengyu Hong

Hierarchical Graph Representation Learning with Differentiable Pooling

Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton

A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization

Zhize Li, Jian Li

Stochastic Spectral and Conjugate Descent Methods

Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov, Elnur Gasanov

Verifiable Reinforcement Learning via Policy Extraction

Osbert Bastani, Yewen Pu, Armando Solar-Lezama

Data-Efficient Hierarchical Reinforcement Learning

Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, Sergey Levine

Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

Hajin Shim, Sung Ju Hwang, Eunho Yang

Variational Memory Encoder-Decoder

Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh

FRAGE: Frequency-Agnostic Word Representation

Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang

Generative Neural Machine Translation

Harshil Shah, David Barber

Recurrent Transformer Networks for Semantic Correspondence

Seungryong Kim, Stephen Lin, Dongbo Min, Kwanghoon Sohn, Sangryul Jeon

Are GANs Created Equal? A Large-Scale Study

Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet

Connectionist Temporal Classification with Maximum Entropy Regularization

Hu Liu, Sheng Jin, Changshui Zhang

Kalman Normalization: Normalizing Internal Representations Across Network Layers

Guangrun Wang, jiefeng peng, Ping Luo, Xinjiang Wang, Liang Lin

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, Eric P. Xing, Eric P Xing

Improved Expressivity Through Dendritic Neural Networks

Xundong Wu, Xiangwen Liu, Wei Li, Qing Wu

Relational recurrent neural networks

Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Pérolat, Karl Tuyls

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

Mikhail Belkin, Daniel J. Hsu, Partha Mitra

Dynamic Network Model from Partial Observations

Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec

Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling

Shannon McCurdy