NeurIPS 2018

1,009 papers

Neural Architecture Optimization

Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-yan Liu

Visual Object Networks: Image Generation with Disentangled 3D Representations

Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba

Implicit Reparameterization Gradients

Mikhail Figurnov, Shakir Mohamed, Andriy Mnih

Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization

Yuanxiang Gao, Li Chen, Baochun Li

SplineNets: Continuous Neural Decision Graphs

Cem Keskin, Shahram Izadi

Banach Wasserstein GAN

Jonas Adler, Sebastian Lunz

Efficient Online Portfolio with Logarithmic Regret

Haipeng Luo, Chen-Yu Wei, Kai Zheng

Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games

Yun Kuen Cheung

MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models

Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang

Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning

yunlong yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang

Compact Generalized Non-local Network

Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding

How Does Batch Normalization Help Optimization?

Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

Adaptive Methods for Nonconvex Optimization

Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar

Transfer of Value Functions via Variational Methods

Andrea Tirinzoni, Rafael Rodriguez Sanchez, Marcello Restelli

Large-Scale Stochastic Sampling from the Probability Simplex

Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

Kevin Scaman, Francis Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee

PAC-learning in the presence of adversaries

Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal

Neighbourhood Consensus Networks

Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla

Precision and Recall for Time Series

Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

Deep Defense: Training DNNs with Improved Adversarial Robustness

Ziang Yan, Yiwen Guo, Changshui Zhang

Learning Compressed Transforms with Low Displacement Rank

Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Re

Deep Neural Networks with Box Convolutions

Egor Burkov, Victor Lempitsky

Probabilistic Model-Agnostic Meta-Learning

Chelsea Finn, Kelvin Xu, Sergey Levine

The Sparse Manifold Transform

Yubei Chen, Dylan Paiton, Bruno Olshausen

Does mitigating ML's impact disparity require treatment disparity?

Zachary Lipton, Julian McAuley, Alexandra Chouldechova

Modeling Dynamic Missingness of Implicit Feedback for Recommendation

Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang

Chaining Mutual Information and Tightening Generalization Bounds

Amir Asadi, Emmanuel Abbe, Sergio Verdu

Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search

Zhuwen Li, Qifeng Chen, Vladlen Koltun

Boosted Sparse and Low-Rank Tensor Regression

Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang

Causal Discovery from Discrete Data using Hidden Compact Representation

Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

Constructing Unrestricted Adversarial Examples with Generative Models

Yang Song, Rui Shu, Nate Kushman, Stefano Ermon

Rectangular Bounding Process

Xuhui Fan, Bin Li, Scott SIsson

Reward learning from human preferences and demonstrations in Atari

Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg

Geometry-Aware Recurrent Neural Networks for Active Visual Recognition

Ricson Cheng, Ziyan Wang, Katerina Fragkiadaki

Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities

Yunwen Lei, Ke Tang

A Neural Compositional Paradigm for Image Captioning

Bo Dai, Sanja Fidler, Dahua Lin

Multi-armed Bandits with Compensation

Siwei Wang, Longbo Huang

Estimating Learnability in the Sublinear Data Regime

Weihao Kong, Gregory Valiant

The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models

Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K Ravikumar, Eric P. Xing

A no-regret generalization of hierarchical softmax to extreme multi-label classification

Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski

Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation

Jing Li, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau

Learning Confidence Sets using Support Vector Machines

Wenbo Wang, Xingye Qiao

ℓ_1-regression with Heavy-tailed Distributions

Lijun Zhang, Zhi-Hua Zhou

Watch Your Step: Learning Node Embeddings via Graph Attention

Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A Alemi

Representer Point Selection for Explaining Deep Neural Networks

Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, Pradeep K Ravikumar

Online Robust Policy Learning in the Presence of Unknown Adversaries

Aaron Havens, Zhanhong Jiang, Soumik Sarkar

BourGAN: Generative Networks with Metric Embeddings

Chang Xiao, Peilin Zhong, Changxi Zheng

Model-based targeted dimensionality reduction for neuronal population data

Mikio Aoi, Jonathan W Pillow

Stochastic Chebyshev Gradient Descent for Spectral Optimization

Insu Han, Haim Avron, Jinwoo Shin

TETRIS: TilE-matching the TRemendous Irregular Sparsity

Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang

Faithful Inversion of Generative Models for Effective Amortized Inference

Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth

How to tell when a clustering is (approximately) correct using convex relaxations

Marina Meila

Benefits of over-parameterization with EM

Ji Xu, Daniel J. Hsu, Arian Maleki

Bayesian Distributed Stochastic Gradient Descent

Michael Teng, Frank Wood

Causal Inference with Noisy and Missing Covariates via Matrix Factorization

Nathan Kallus, Xiaojie Mao, Madeleine Udell

Policy Regret in Repeated Games

Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri

Evolution-Guided Policy Gradient in Reinforcement Learning

Shauharda Khadka, Kagan Tumer

Adversarial Attacks on Stochastic Bandits

Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu

Trajectory Convolution for Action Recognition

Yue Zhao, Yuanjun Xiong, Dahua Lin

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin

A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents

YAN ZHENG, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang

Adversarial vulnerability for any classifier

Alhussein Fawzi, Hamza Fawzi, Omar Fawzi

The committee machine: Computational to statistical gaps in learning a two-layers neural network

Benjamin Aubin, Antoine Maillard, jean barbier, Florent Krzakala, Nicolas Macris

Beauty-in-averageness and its contextual modulations: A Bayesian statistical account

Chaitanya Ryali, Angela J. Yu

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds

Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli

Horizon-Independent Minimax Linear Regression

Alan Malek, Peter L. Bartlett, Peter L Bartlett

A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang

End-to-End Differentiable Physics for Learning and Control

Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter

Empirical Risk Minimization Under Fairness Constraints

Michele Donini, Luca Oneto, Shai Ben-David, Massimiliano Pontil, John S. Shawe-taylor

A Convex Duality Framework for GANs

Farzan Farnia, David Tse

SLAYER: Spike Layer Error Reassignment in Time

Sumit Bam Shrestha, Garrick Orchard

HitNet: Hybrid Ternary Recurrent Neural Network

Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang

A Bandit Approach to Sequential Experimental Design with False Discovery Control

Kevin G. Jamieson, Lalit Jain

Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation

Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu

Learning from Group Comparisons: Exploiting Higher Order Interactions

Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-jui Hsieh

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models

Amir Dezfouli, Richard Morris, Fabio T Ramos, Peter Dayan, Bernard Balleine

Bilevel Distance Metric Learning for Robust Image Recognition

Jie Xu, Lei Luo, Cheng Deng, Heng Huang

Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks

Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-fu Chang

Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units

Yixi Xu, Xiao Wang

The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation

Zi Yin, Vin Sachidananda, Balaji Prabhakar

Distributed Multitask Reinforcement Learning with Quadratic Convergence

Rasul Tutunov, Dongho Kim, Haitham Bou Ammar

Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels

Shahin Shahrampour, Vahid Tarokh

Learning to Teach with Dynamic Loss Functions

Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin

With Friends Like These, Who Needs Adversaries?

Saumya Jetley, Nicholas Lord, Philip Torr

Deep Generative Models for Distribution-Preserving Lossy Compression

Michael Tschannen, Eirikur Agustsson, Mario Lucic

Towards Deep Conversational Recommendations

Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin

Inequity aversion improves cooperation in intertemporal social dilemmas

Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Duenez-Guzman

Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation

Zhiqiang Xu

Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection

Taylor Mordan, Nicolas Thome, Gilles Henaff, Matthieu Cord

RetGK: Graph Kernels based on Return Probabilities of Random Walks

Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai

Exact natural gradient in deep linear networks and its application to the nonlinear case

Alberto Bernacchia, Máté Lengyel, Guillaume Hennequin

DropBlock: A regularization method for convolutional networks

Golnaz Ghiasi, Tsung-Yi Lin, Quoc V Le

Visual Reinforcement Learning with Imagined Goals

Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin

Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes

Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian Ziebart

Fully Understanding The Hashing Trick

Casper B. Freksen, Lior Kamma, Kasper Green Larsen

Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint

Rajan Udwani

Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates

Quoc Tran Dinh

Differentially Private Testing of Identity and Closeness of Discrete Distributions

Jayadev Acharya, Ziteng Sun, Huanyu Zhang

Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments

Daniel Johnson, Daniel Gorelik, Ross E Mawhorter, Kyle Suver, Weiqing Gu