NeurIPS 2017

679 papers

Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee

Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg

Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions

Maria-Florina F Balcan, Hongyang Zhang

A General Framework for Robust Interactive Learning

Ehsan Emamjomeh-Zadeh, David Kempe

Attention is All you Need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones

Robust Conditional Probabilities

Yoav Wald, Amir Globerson

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods

Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L Sharpnack, Ryan J Tibshirani

Mixture-Rank Matrix Approximation for Collaborative Filtering

Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu

Practical Locally Private Heavy Hitters

Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta

The Scaling Limit of High-Dimensional Online Independent Component Analysis

Chuang Wang, Yue Lu

Neural Program Meta-Induction

Jacob Devlin, Rudy R Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli

Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach

Slobodan Mitrovic, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub M Tarnawski, Volkan Cevher

Online Prediction with Selfish Experts

Tim Roughgarden, Okke Schrijvers

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen

Adaptive Clustering through Semidefinite Programming

Martin Royer

Riemannian approach to batch normalization

Minhyung Cho, Jaehyung Lee

A Greedy Approach for Budgeted Maximum Inner Product Search

Hsiang-Fu Yu, Cho-jui Hsieh, Qi Lei, Inderjit S Dhillon

Learning with Bandit Feedback in Potential Games

Amélie Heliou, Johanne Cohen, Panayotis Mertikopoulos

Shallow Updates for Deep Reinforcement Learning

Nir Levine, Tom Zahavy, Daniel J Mankowitz, Aviv Tamar, Shie Mannor

Variational Memory Addressing in Generative Models

Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende

Invariance and Stability of Deep Convolutional Representations

Alberto Bietti, Julien Mairal

On Structured Prediction Theory with Calibrated Convex Surrogate Losses

Anton Osokin, Francis Bach, Simon Lacoste-Julien

Deep Mean-Shift Priors for Image Restoration

Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin

Effective Parallelisation for Machine Learning

Michael Kamp, Mario Boley, Olana Missura, Thomas Gärtner

Independence clustering (without a matrix)

Daniil Ryabko

Online Reinforcement Learning in Stochastic Games

Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu

Solving Most Systems of Random Quadratic Equations

Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen

Efficient Second-Order Online Kernel Learning with Adaptive Embedding

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Optimistic posterior sampling for reinforcement learning: worst-case regret bounds

Shipra Agrawal, Randy Jia

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, Jimmy Ba

A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks

Qinliang Su, Xuejun Liao, Lawrence Carin

Variational Inference via χ Upper Bound Minimization

Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei

Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting

Yue Wang, Wei Chen, Yuting Liu, Zhi-ming Ma, Tie-yan Liu

Hypothesis Transfer Learning via Transformation Functions

Simon S Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning

Xingguo Li, Lin Yang, Jason Ge, Jarvis Haupt, Tong Zhang

Successor Features for Transfer in Reinforcement Learning

Andre Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul

Pose Guided Person Image Generation

Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars

Affine-Invariant Online Optimization and the Low-rank Experts Problem

Tomer Koren, Roi Livni

Spectral Mixture Kernels for Multi-Output Gaussian Processes

Gabriel Parra, Felipe Tobar

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls

The power of absolute discounting: all-dimensional distribution estimation

Moein Falahatgar, Mesrob I Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati

Inverse Reward Design

Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca Dragan

The Expressive Power of Neural Networks: A View from the Width

Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang

Dilated Recurrent Neural Networks

Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo

Statistical Cost Sharing

Eric Balkanski, Umar Syed, Sergei Vassilvitskii

Min-Max Propagation

Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey

Reducing Reparameterization Gradient Variance

Andrew Miller, Nicholas Foti, Alexander D'Amour, Ryan P. Adams

Sparse Embedded k-Means Clustering

Weiwei Liu, Xiaobo Shen, Ivor Tsang

Alternating minimization for dictionary learning with random initialization

Niladri Chatterji, Peter L. Bartlett, Peter L Bartlett

Bayesian GAN

Yunus Saatci, Andrew G Wilson

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

Alex M Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Kristof Schütt, Pieter-jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko

Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum

f-GANs in an Information Geometric Nutshell

Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, Robert C. Williamson

Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification

Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J Kim, Johannes Fürnkranz

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

Federico Monti, Michael Bronstein, Xavier Bresson

Affinity Clustering: Hierarchical Clustering at Scale

Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris

Countering Feedback Delays in Multi-Agent Learning

Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin

Unsupervised Learning of Disentangled Representations from Video

Emily L Denton, vighnesh Birodkar

Style Transfer from Non-Parallel Text by Cross-Alignment

Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola

A Regularized Framework for Sparse and Structured Neural Attention

Vlad Niculae, Mathieu Blondel

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

Yunzhu Li, Jiaming Song, Stefano Ermon

Dynamic Routing Between Capsules

Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton

Sharpness, Restart and Acceleration

Vincent Roulet, Alexandre D'aspremont

Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes

Jeremiah Liu, Brent Coull

Predictive State Recurrent Neural Networks

Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li

Population Matching Discrepancy and Applications in Deep Learning

Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu

Decoding with Value Networks for Neural Machine Translation

Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang

A multi-agent reinforcement learning model of common-pool resource appropriation

Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls

Group Additive Structure Identification for Kernel Nonparametric Regression

Chao Pan, Michael Zhu

Reliable Decision Support using Counterfactual Models

Peter Schulam, Suchi Saria

Lower bounds on the robustness to adversarial perturbations

Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys

Experimental Design for Learning Causal Graphs with Latent Variables

Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

Scalable Generalized Linear Bandits: Online Computation and Hashing

Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett

Deep Dynamic Poisson Factorization Model

Chengyue Gong, win-bin huang

Permutation-based Causal Inference Algorithms with Interventions

Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler

Reconstruct & Crush Network

Erinc Merdivan, Mohammad Reza Loghmani, Matthieu Geist

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Alex Kendall, Yarin Gal

Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition

Mingrui Liu, Tianbao Yang

Parallel Streaming Wasserstein Barycenters

Matthew Staib, Sebastian Claici, Justin M Solomon, Stefanie Jegelka

Collecting Telemetry Data Privately

Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin

Recycling Privileged Learning and Distribution Matching for Fairness

Novi Quadrianto, Viktoriia Sharmanska

Gradient Methods for Submodular Maximization

Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi

Question Asking as Program Generation

Anselm Rothe, Brenden M Lake, Todd Gureckis

A-NICE-MC: Adversarial Training for MCMC

Jiaming Song, Shengjia Zhao, Stefano Ermon

Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions

M. Sevi Baltaoglu, Lang Tong, Qing Zhao

DPSCREEN: Dynamic Personalized Screening

Kartik Ahuja, William Zame, Mihaela van der Schaar

Online Learning with a Hint

Ofer Dekel, arthur flajolet, Nika Haghtalab, Patrick Jaillet

Maximum Margin Interval Trees

Alexandre Drouin, Toby Hocking, François Laviolette

Multi-View Decision Processes: The Helper-AI Problem

Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes

Taylor W Killian, Samuel Daulton, George Konidaris, Finale Doshi-velez

Few-Shot Adversarial Domain Adaptation

Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto

Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization

Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han

Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding

Arya Mazumdar, Soumyabrata Pal

Learned in Translation: Contextualized Word Vectors

Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher

Uprooting and Rerooting Higher-Order Graphical Models

Mark Rowland, Adrian Weller

Process-constrained batch Bayesian optimisation

Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti

Dynamic Importance Sampling for Anytime Bounds of the Partition Function

Qi Lou, Rina Dechter, Alex Ihler

Learning from Complementary Labels

Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama

Elementary Symmetric Polynomials for Optimal Experimental Design

Zelda E. Mariet, Suvrit Sra