NeurIPS 2016

569 papers

Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation

Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher

Generative Adversarial Imitation Learning

Jonathan Ho, Stefano Ermon

Large-Scale Price Optimization via Network Flow

Shinji Ito, Ryohei Fujimaki

Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization

Tyler B Johnson, Carlos Guestrin

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

Shahin Jabbari, Ryan M Rogers, Aaron Roth, Steven Z. Wu

The Product Cut

Thomas Laurent, James von Brecht, Xavier Bresson, arthur szlam

A scaled Bregman theorem with applications

Richard Nock, Aditya Menon, Cheng Soon Ong

Fast and Flexible Monotonic Functions with Ensembles of Lattices

Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta

Optimal Tagging with Markov Chain Optimization

Nir Rosenfeld, Amir Globerson

New Liftable Classes for First-Order Probabilistic Inference

Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole

The Power of Optimization from Samples

Eric Balkanski, Aviad Rubinstein, Yaron Singer

Barzilai-Borwein Step Size for Stochastic Gradient Descent

Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, Shimon Whiteson

Without-Replacement Sampling for Stochastic Gradient Methods

Ohad Shamir

A Probabilistic Framework for Deep Learning

Ankit B Patel, Minh Tan Nguyen, Richard Baraniuk

LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain

Zeyuan Allen-Zhu, Yuanzhi Li

Sample Complexity of Automated Mechanism Design

Maria-Florina F Balcan, Tuomas Sandholm, Ellen Vitercik

Optimizing affinity-based binary hashing using auxiliary coordinates

Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán

Stochastic Online AUC Maximization

Yiming Ying, Longyin Wen, Siwei Lyu

Swapout: Learning an ensemble of deep architectures

Saurabh Singh, Derek Hoiem, David Forsyth

Clustering with Bregman Divergences: an Asymptotic Analysis

Chaoyue Liu, Mikhail Belkin

Strategic Attentive Writer for Learning Macro-Actions

Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals

Deep Learning Games

Dale Schuurmans, Martin A Zinkevich

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Xiang Li, Tao Qin, Jian Yang, Tie-yan Liu

Safe and Efficient Off-Policy Reinforcement Learning

Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc Bellemare

Cooperative Inverse Reinforcement Learning

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

Simple and Efficient Weighted Minwise Hashing

Anshumali Shrivastava

Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images

Junhua Mao, Jiajing Xu, Kevin Jing, Alan L. Yuille

Value Iteration Networks

Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel

Learning to Poke by Poking: Experiential Learning of Intuitive Physics

Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine

DISCO Nets : DISsimilarity COefficients Networks

Diane Bouchacourt, Pawan K Mudigonda, Sebastian Nowozin

A Non-generative Framework and Convex Relaxations for Unsupervised Learning

Elad Hazan, Tengyu Ma

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning

Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama

Stochastic Variational Deep Kernel Learning

Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing, Eric P Xing

Pairwise Choice Markov Chains

Stephen Ragain, Johan Ugander

Regularized Nonlinear Acceleration

Damien Scieur, Alexandre D'aspremont, Francis Bach

Error Analysis of Generalized Nyström Kernel Regression

Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai

Normalized Spectral Map Synchronization

Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi

Kronecker Determinantal Point Processes

Zelda E. Mariet, Suvrit Sra

Local Minimax Complexity of Stochastic Convex Optimization

sabyasachi chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu

Brains on Beats

Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven

Doubly Convolutional Neural Networks

Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang, Weining Lu

Dimensionality Reduction of Massive Sparse Datasets Using Coresets

Dan Feldman, Mikhail Volkov, Daniela Rus

Fast Algorithms for Robust PCA via Gradient Descent

Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/ε)

Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

On Robustness of Kernel Clustering

Bowei Yan, Purnamrita Sarkar

Learning Transferrable Representations for Unsupervised Domain Adaptation

Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese

Showing versus doing: Teaching by demonstration

Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil

Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games

Sougata Chaudhuri, Ambuj Tewari

Universal Correspondence Network

Christopher B Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning

Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

Causal Bandits: Learning Good Interventions via Causal Inference

Finnian Lattimore, Tor Lattimore, Mark D. Reid

Learning in Games: Robustness of Fast Convergence

Dylan J Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Qiang Liu, Dilin Wang

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories

Yuan Zhao, ll Memming Park

Optimistic Bandit Convex Optimization

Scott Yang, Mehryar Mohri

Tensor Switching Networks

Chuan-Yung Tsai, Andrew M Saxe, David Cox

Global Optimality of Local Search for Low Rank Matrix Recovery

Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Conditional Image Generation with PixelCNN Decoders

Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals

Multistage Campaigning in Social Networks

Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha

A Bayesian method for reducing bias in neural representational similarity analysis

Mingbo Cai, Nicolas W Schuck, Jonathan W Pillow, Yael Niv

Improved Error Bounds for Tree Representations of Metric Spaces

Samir Chowdhury, Facundo Mémoli, Zane T Smith

Observational-Interventional Priors for Dose-Response Learning

Ricardo Silva

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices

Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing, Eric P Xing

Unifying Count-Based Exploration and Intrinsic Motivation

Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton

Sampling for Bayesian Program Learning

Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum

Long-term Causal Effects via Behavioral Game Theory

Panagiotis Toulis, David C. Parkes

Adversarial Multiclass Classification: A Risk Minimization Perspective

Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart

Efficient Nonparametric Smoothness Estimation

Shashank Singh, Simon S Du, Barnabás Póczos

Stochastic Gradient MCMC with Stale Gradients

Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin

Unsupervised Domain Adaptation with Residual Transfer Networks

Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan, Michael I Jordan

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

Amit Daniely, Roy Frostig, Yoram Singer

VIME: Variational Information Maximizing Exploration

Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck

SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques

Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky

Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products

Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover

Learning Sensor Multiplexing Design through Back-propagation

Ayan Chakrabarti

Combinatorial Multi-Armed Bandit with General Reward Functions

Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu

Statistical Inference for Cluster Trees

Jisu KIM, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman

Bayesian Optimization with Robust Bayesian Neural Networks

Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

A Bio-inspired Redundant Sensing Architecture

Anh Tuan Nguyen, Jian Xu, Zhi Yang

Learning What and Where to Draw

Scott E Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele

Relevant sparse codes with variational information bottleneck

Matthew Chalk, Olivier Marre, Gasper Tkacik

Optimal Cluster Recovery in the Labeled Stochastic Block Model

Se-Young Yun, Alexandre Proutiere

Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats

Pulkit Tandon, Yash H Malviya, Bipin Rajendran

Adaptive Smoothed Online Multi-Task Learning

Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang

Differential Privacy without Sensitivity

Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa

Solving Marginal MAP Problems with NP Oracles and Parity Constraints

Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman

Memory-Efficient Backpropagation Through Time

Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, Adam T Kalai

Graphons, mergeons, and so on!

Justin Eldridge, Mikhail Belkin, Yusu Wang

Scalable Adaptive Stochastic Optimization Using Random Projections

Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M Buhmann, Nicolai Meinshausen

Learning Deep Parsimonious Representations

Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun

Blind Attacks on Machine Learners

Alex Beatson, Zhaoran Wang, Han Liu

Inference by Reparameterization in Neural Population Codes

Rajkumar Vasudeva Raju, Zachary Pitkow

Batched Gaussian Process Bandit Optimization via Determinantal Point Processes

Tarun Kathuria, Amit Deshpande, Pushmeet Kohli

Deep Learning Models of the Retinal Response to Natural Scenes

Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus

Learned Region Sparsity and Diversity Also Predicts Visual Attention

Zijun Wei, Hossein Adeli, Minh Hoai Nguyen, Greg Zelinsky, Dimitris Samaras

Geometric Dirichlet Means Algorithm for topic inference

Mikhail Yurochkin, Xuanlong Nguyen

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability

Guillaume Papa, Aurélien Bellet, Stéphan Clémençon