NeurIPS 2018

1,009 papers

Improving Explorability in Variational Inference with Annealed Variational Objectives

Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation

Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi

Active Matting

Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin Yin

Complex Gated Recurrent Neural Networks

Moritz Wolter, Angela Yao

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort

Mirrored Langevin Dynamics

Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher

MetaReg: Towards Domain Generalization using Meta-Regularization

Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa

A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice

Hendrik Fichtenberger, Dennis Rohde

Connecting Optimization and Regularization Paths

Arun Suggala, Adarsh Prasad, Pradeep K Ravikumar

Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J Mankowitz, Shie Mannor

3D-Aware Scene Manipulation via Inverse Graphics

Shunyu Yao, Tzu Ming Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E. Hinton

On Coresets for Logistic Regression

Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff

Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks

Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny

A Smoother Way to Train Structured Prediction Models

Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui

Stochastic Nonparametric Event-Tensor Decomposition

Shandian Zhe, Yishuai Du

Power-law efficient neural codes provide general link between perceptual bias and discriminability

Michael Morais, Jonathan W Pillow

Neural Proximal Gradient Descent for Compressive Imaging

Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi

Learning Hierarchical Semantic Image Manipulation through Structured Representations

Seunghoon Hong, Xinchen Yan, Thomas S. Huang, Honglak Lee

Adversarially Robust Optimization with Gaussian Processes

Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher

An intriguing failing of convolutional neural networks and the CoordConv solution

Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank

Non-delusional Q-learning and value-iteration

Tyler Lu, Dale Schuurmans, Craig Boutilier

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation

Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu

Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution

Zhisheng Zhong, Tiancheng Shen, Yibo Yang, Zhouchen Lin, Chao Zhang

On Oracle-Efficient PAC RL with Rich Observations

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Deep Anomaly Detection Using Geometric Transformations

Izhak Golan, Ran El-Yaniv

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli

GLoMo: Unsupervised Learning of Transferable Relational Graphs

Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen

Learning Temporal Point Processes via Reinforcement Learning

Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie

Deep State Space Models for Time Series Forecasting

Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang

Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons

Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos Papadimitriou, Amin Saberi

Distributed Stochastic Optimization via Adaptive SGD

Ashok Cutkosky, Róbert Busa-Fekete

Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making

Nishant Desai, Andrew Critch, Stuart Russell

Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss

Stephen Mussmann, Percy Liang

On the Convergence and Robustness of Training GANs with Regularized Optimal Transport

Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason Lee

Randomized Prior Functions for Deep Reinforcement Learning

Ian Osband, John Aslanides, Albin Cassirer

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Arthur Jacot, Franck Gabriel, Clement Hongler

Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

Sequence-to-Segment Networks for Segment Detection

Zijun Wei, Boyu Wang, Minh Hoai Nguyen, Jianming Zhang, Zhe Lin

Posterior Concentration for Sparse Deep Learning

Nicholas G Polson, Veronika Ročková

Uniform Convergence of Gradients for Non-Convex Learning and Optimization

Dylan J Foster, Ayush Sekhari, Karthik Sridharan

An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression

Sheng Chen, Arindam Banerjee

Quadratic Decomposable Submodular Function Minimization

Pan Li, Niao He, Olgica Milenkovic

Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC

Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic

Bayesian Adversarial Learning

Nanyang Ye, Zhanxing Zhu

Meta-Learning MCMC Proposals

Tongzhou Wang, Yi Wu, Dave Moore, Stuart Russell

LinkNet: Relational Embedding for Scene Graph

Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon

Optimal Subsampling with Influence Functions

Daniel Ting, Eric Brochu

Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks

Bryan Lim

SING: Symbol-to-Instrument Neural Generator

Alexandre Defossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis Bach

Bilevel learning of the Group Lasso structure

Jordan Frecon, Saverio Salzo, Massimiliano Pontil

Legendre Decomposition for Tensors

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda

Removing Hidden Confounding by Experimental Grounding

Nathan Kallus, Aahlad Manas Puli, Uri Shalit

Robustness of conditional GANs to noisy labels

Kiran K Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh

On Fast Leverage Score Sampling and Optimal Learning

Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco

Reinforcement Learning of Theorem Proving

Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Miroslav Olšák

Discrimination-aware Channel Pruning for Deep Neural Networks

Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo

Informative Features for Model Comparison

Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf

Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

Yuanzhi Li, Yingyu Liang

Monte-Carlo Tree Search for Constrained POMDPs

Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-eung Kim

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto

A probabilistic population code based on neural samples

Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj, Ralf Haefner

A flexible model for training action localization with varying levels of supervision

Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid

Link Prediction Based on Graph Neural Networks

Muhan Zhang, Yixin Chen

Joint Autoregressive and Hierarchical Priors for Learned Image Compression

David Minnen, Johannes Ballé, George D Toderici

Bias and Generalization in Deep Generative Models: An Empirical Study

Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman

Single-Agent Policy Tree Search With Guarantees

Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber

Regularizing by the Variance of the Activations' Sample-Variances

Etai Littwin, Lior Wolf

On Learning Intrinsic Rewards for Policy Gradient Methods

Zeyu Zheng, Junhyuk Oh, Satinder Singh

Generalizing Graph Matching beyond Quadratic Assignment Model

Tianshu Yu, Junchi Yan, Yilin Wang, Wei Liu, baoxin Li

From Stochastic Planning to Marginal MAP

Hao(Jackson) Cui, Radu Marinescu, Roni Khardon

Completing State Representations using Spectral Learning

Nan Jiang, Alex Kulesza, Satinder Singh

Online Learning with an Unknown Fairness Metric

Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth

Regularization Learning Networks: Deep Learning for Tabular Datasets

Ira Shavitt, Eran Segal

Deep Poisson gamma dynamical systems

Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou

Reversible Recurrent Neural Networks

Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B Grosse

A Lyapunov-based Approach to Safe Reinforcement Learning

Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation

Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin

Approximate Knowledge Compilation by Online Collapsed Importance Sampling

Tal Friedman, Guy Van den Broeck

Beyond Grids: Learning Graph Representations for Visual Recognition

Yin Li, Abhinav Gupta

Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra

John T Halloran, David M Rocke

Unsupervised Attention-guided Image-to-Image Translation

Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Unsupervised Learning of Shape and Pose with Differentiable Point Clouds

Eldar Insafutdinov, Alexey Dosovitskiy

The Physical Systems Behind Optimization Algorithms

Lin Yang, Raman Arora, Vladimir braverman, Tuo Zhao

Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features

Mojmir Mutny, Andreas Krause

MetaGAN: An Adversarial Approach to Few-Shot Learning

Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization

Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow

Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence

Trong Dinh Thac Do, Longbing Cao

Meta-Reinforcement Learning of Structured Exploration Strategies

Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, Sergey Levine

Synaptic Strength For Convolutional Neural Network

CHEN LIN, Zhao Zhong, Wu Wei, Junjie Yan

e-SNLI: Natural Language Inference with Natural Language Explanations

Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom

Sparse DNNs with Improved Adversarial Robustness

Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen

Measures of distortion for machine learning

Leena Chennuru Vankadara, Ulrike Von Luxburg

Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

Yining Wang, Sivaraman Balakrishnan, Aarti Singh

Explaining Deep Learning Models -- A Bayesian Non-parametric Approach

Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin

Temporal Regularization for Markov Decision Process

Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup

Exponentially Weighted Imitation Learning for Batched Historical Data

Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu

Learning Conditioned Graph Structures for Interpretable Visual Question Answering

Will Norcliffe-Brown, Stathis Vafeias, Sarah Parisot

The Everlasting Database: Statistical Validity at a Fair Price

Blake E Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro

Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression

Neha Gupta, Aaron Sidford