NeurIPS 2018

1,009 papers

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding

Nan Rosemary Ke, Anirudh Goyal ALIAS PARTH GOYAL, Olexa Bilaniuk, Jonathan Binas, Michael Mozer

A^2-Nets: Double Attention Networks

Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng

Direct Estimation of Differences in Causal Graphs

Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler

Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem

Victor-Emmanuel Brunel

Bayesian Model-Agnostic Meta-Learning

Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio

Towards Robust Detection of Adversarial Examples

Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu

Invertibility of Convolutional Generative Networks from Partial Measurements

Fangchang Ma, Ulas Ayaz, Sertac Karaman

Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo

Oren Mangoubi, Nisheeth Vishnoi

Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing, Eric P Xing

Acceleration through Optimistic No-Regret Dynamics

Jun-Kun Wang, Jacob D. Abernethy

Fast greedy algorithms for dictionary selection with generalized sparsity constraints

Kaito Fujii, Tasuku Soma

Learning to Navigate in Cities Without a Map

Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson

Point process latent variable models of larval zebrafish behavior

Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman

Practical Methods for Graph Two-Sample Testing

Debarghya Ghoshdastidar, Ulrike Von Luxburg

Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning

Ofir Marom, Benjamin Rosman

Deep Generative Markov State Models

Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe

Using Large Ensembles of Control Variates for Variational Inference

Tomas Geffner, Justin Domke

Minimax Estimation of Neural Net Distance

Kaiyi Ji, Yingbin Liang

rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions

Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye

TopRank: A practical algorithm for online stochastic ranking

Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation

Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

Expanding Holographic Embeddings for Knowledge Completion

Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal

Image-to-image translation for cross-domain disentanglement

Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio

DeepProbLog: Neural Probabilistic Logic Programming

Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt

Active Learning for Non-Parametric Regression Using Purely Random Trees

Jack Goetz, Ambuj Tewari, Paul Zimmerman

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi

Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity

Laming Chen, Guoxin Zhang, Eric Zhou

Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues

Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan

Hyperbolic Neural Networks

Octavian Ganea, Gary Becigneul, Thomas Hofmann

Approximating Real-Time Recurrent Learning with Random Kronecker Factors

Asier Mujika, Florian Meier, Angelika Steger

Derivative Estimation in Random Design

Yu Liu, Kris De Brabanter

Stochastic Cubic Regularization for Fast Nonconvex Optimization

Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization

Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney

On the Local Minima of the Empirical Risk

Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan, Michael I Jordan

Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization

Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li

Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices

Don Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain

The emergence of multiple retinal cell types through efficient coding of natural movies

Samuel Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny

Adversarial Regularizers in Inverse Problems

Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb

Bandit Learning with Implicit Feedback

Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun

Occam's razor is insufficient to infer the preferences of irrational agents

Stuart Armstrong, Sören Mindermann

Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models

Yining Wang, Xi Chen, Yuan Zhou

Video-to-Video Synthesis

Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao

How to Start Training: The Effect of Initialization and Architecture

Boris Hanin, David Rolnick

Deep Generative Models with Learnable Knowledge Constraints

Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, LIANHUI Qin, Xiaodan Liang

Neural Networks Trained to Solve Differential Equations Learn General Representations

Martin Magill, Faisal Qureshi, Hendrick de Haan

Virtual Class Enhanced Discriminative Embedding Learning

Binghui Chen, Weihong Deng, Haifeng Shen

Tree-to-tree Neural Networks for Program Translation

Xinyun Chen, Chang Liu, Dawn Song

Exploration in Structured Reinforcement Learning

Jungseul Ok, Alexandre Proutiere, Damianos Tranos

Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language

Seonghyeon Nam, Yunji Kim, Seon Joo Kim

Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base

Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

Spectral Filtering for General Linear Dynamical Systems

Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, Jure Leskovec

Blind Deconvolutional Phase Retrieval via Convex Programming

Ali Ahmed, Alireza Aghasi, Paul Hand

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Robert Gower, Filip Hanzely, Peter Richtarik, Sebastian U Stich

Lipschitz regularity of deep neural networks: analysis and efficient estimation

Aladin Virmaux, Kevin Scaman

Genetic-Gated Networks for Deep Reinforcement Learning

Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak

NEON2: Finding Local Minima via First-Order Oracles

Zeyuan Allen-Zhu, Yuanzhi Li

Learning Invariances using the Marginal Likelihood

Mark van der Wilk, Matthias Bauer, ST John, James Hensman

Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch

Preference Based Adaptation for Learning Objectives

Yao-Xiang Ding, Zhi-Hua Zhou

Is Q-Learning Provably Efficient?

Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan, Michael I Jordan

On Learning Markov Chains

Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan

Partially-Supervised Image Captioning

Peter Anderson, Stephen Gould, Mark Johnson

Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling

Yunzhe Tao, Qi Sun, Qiang Du, Wei Liu

Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization

Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao

Temporal alignment and latent Gaussian process factor inference in population spike trains

Lea Duncker, Maneesh Sahani

Do Less, Get More: Streaming Submodular Maximization with Subsampling

Moran Feldman, Amin Karbasi, Ehsan Kazemi

Efficient Projection onto the Perfect Phylogeny Model

Bei Jia, Surjyendu Ray, Sam Safavi, José Bento

Glow: Generative Flow with Invertible 1x1 Convolutions

Diederik P. Kingma, Prafulla Dhariwal

KONG: Kernels for ordered-neighborhood graphs

Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic

The Importance of Sampling inMeta-Reinforcement Learning

Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan

Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning

Tyler Scott, Karl Ridgeway, Michael Mozer

Neural Edit Operations for Biological Sequences

Satoshi Koide, Keisuke Kawano, Takuro Kutsuna

Efficient Neural Network Robustness Certification with General Activation Functions

Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-jui Hsieh, Luca Daniel

Optimization for Approximate Submodularity

Yaron Singer, Avinatan Hassidim

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds

Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin

GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training

Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li

Reinforced Continual Learning

Ju Xu, Zhanxing Zhu

Variance-Reduced Stochastic Gradient Descent on Streaming Data

Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip Gibbons, Srikanta Tirthapura

PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

Bianca Dumitrascu, Karen Feng, Barbara Engelhardt

Sequential Context Encoding for Duplicate Removal

Lu Qi, Shu Liu, Jianping Shi, Jiaya Jia

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

Rad Niazadeh, Tim Roughgarden, Joshua Wang

Learning Abstract Options

Matthew Riemer, Miao Liu, Gerald Tesauro

Re-evaluating evaluation

David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel

Efficient inference for time-varying behavior during learning

Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W Pillow

A Retrieve-and-Edit Framework for Predicting Structured Outputs

Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

High Dimensional Linear Regression using Lattice Basis Reduction

Ilias Zadik, David Gamarnik

Domain-Invariant Projection Learning for Zero-Shot Recognition

An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang

Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation

Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh

Orthogonally Decoupled Variational Gaussian Processes

Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth

The Price of Fair PCA: One Extra dimension

Samira Samadi, Uthaipon Tantipongpipat, Jamie H Morgenstern, Mohit Singh, Santosh Vempala

Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements

Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar

Symbolic Graph Reasoning Meets Convolutions

Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing

Sketching Method for Large Scale Combinatorial Inference

Wei Sun, Junwei Lu, Han Liu

Breaking the Activation Function Bottleneck through Adaptive Parameterization

Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot

Diminishing Returns Shape Constraints for Interpretability and Regularization

Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini

MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval

Helena Peic Tukuljac, Antoine Deleforge, Remi Gribonval

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine

Adversarial Scene Editing: Automatic Object Removal from Weak Supervision

Rakshith R Shetty, Mario Fritz, Bernt Schiele