NeurIPS 2016

569 papers

Domain Separation Networks

Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan

Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

Weihao Gao, Sewoong Oh, Pramod Viswanath

Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

Hongseok Namkoong, John C. Duchi

Optimistic Gittins Indices

Eli Gutin, Vivek Farias

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum

Online Pricing with Strategic and Patient Buyers

Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar

Catching heuristics are optimal control policies

Boris Belousov, Gerhard Neumann, Constantin A Rothkopf, Jan R. Peters, Jan R Peters

Asynchronous Parallel Greedy Coordinate Descent

Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon

Dual Space Gradient Descent for Online Learning

Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung

Regret of Queueing Bandits

Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai

How Deep is the Feature Analysis underlying Rapid Visual Categorization?

Sven Eberhardt, Jonah G Cader, Thomas Serre

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis

Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

Statistical Inference for Pairwise Graphical Models Using Score Matching

Ming Yu, Mladen Kolar, Varun Gupta

Tracking the Best Expert in Non-stationary Stochastic Environments

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

Reconstructing Parameters of Spreading Models from Partial Observations

Andrey Lokhov

Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah Goodman

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Jack Rae, Jonathan J Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior

Spatiotemporal Residual Networks for Video Action Recognition

Christoph Feichtenhofer, Axel Pinz, Richard Wildes

Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games

Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen

The non-convex Burer-Monteiro approach works on smooth semidefinite programs

Nicolas Boumal, Vlad Voroninski, Afonso Bandeira

Multi-step learning and underlying structure in statistical models

Maia Fraser

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan

Designing smoothing functions for improved worst-case competitive ratio in online optimization

Reza Eghbali, Maryam Fazel

Bayesian optimization for automated model selection

Gustavo Malkomes, Charles Schaff, Roman Garnett

Disease Trajectory Maps

Peter Schulam, Raman Arora

Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics

Travis Monk, Cristina Savin, Jörg Lücke

Synthesis of MCMC and Belief Propagation

Michael Chertkov, Jinwoo Shin, Sungsoo Ahn, Sung-Soo Ahn

Pruning Random Forests for Prediction on a Budget

Feng Nan, Joseph Wang, Venkatesh Saligrama

Completely random measures for modelling block-structured sparse networks

Tue Herlau, Mikkel N Schmidt, Morten Mørup

Diffusion-Convolutional Neural Networks

James Atwood, Don Towsley

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences

Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent

Chi Jin, Sham M. Kakade, Praneeth Netrapalli

Low-Rank Regression with Tensor Responses

Guillaume Rabusseau, Hachem Kadri

Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Andreas Veit, Michael J Wilber, Serge Belongie

Generating Images with Perceptual Similarity Metrics based on Deep Networks

Alexey Dosovitskiy, Thomas Brox

Feature-distributed sparse regression: a screen-and-clean approach

Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun

CNNpack: Packing Convolutional Neural Networks in the Frequency Domain

Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu

A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++

Dennis Wei

MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

Gregory Rogez, Cordelia Schmid

Optimal spectral transportation with application to music transcription

Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya

Robust Spectral Detection of Global Structures in the Data by Learning a Regularization

Pan Zhang

Beyond Exchangeability: The Chinese Voting Process

Moontae Lee, Seok Hyun Jin, David Mimno

Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing

Farshad Lahouti, Babak Hassibi

An Efficient Streaming Algorithm for the Submodular Cover Problem

Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh

Learning Deep Embeddings with Histogram Loss

Evgeniya Ustinova, Victor Lempitsky

Coin Betting and Parameter-Free Online Learning

Francesco Orabona, Dávid Pál

Bayesian Optimization for Probabilistic Programs

Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, Frank Wood

Combinatorial Energy Learning for Image Segmentation

Jeremy B Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel

Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian

Victor Picheny, Robert B. Gramacy, Stefan Wild, Sebastien Le Digabel

Interaction Networks for Learning about Objects, Relations and Physics

Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu

An Online Sequence-to-Sequence Model Using Partial Conditioning

Navdeep Jaitly, Quoc V Le, Oriol Vinyals, Ilya Sutskever, David Sussillo

Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning

Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen

Reward Augmented Maximum Likelihood for Neural Structured Prediction

Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster

Structure-Blind Signal Recovery

Dmitry Ostrovsky, Zaïd Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski

Refined Lower Bounds for Adversarial Bandits

Sébastien Gerchinovitz, Tor Lattimore

Adaptive Concentration Inequalities for Sequential Decision Problems

Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

Regret Bounds for Non-decomposable Metrics with Missing Labels

Nagarajan Natarajan, Prateek Jain

Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution

Christopher Lynn, Daniel D. Lee, Daniel D Lee

Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

Shashank Singh, Barnabás Póczos

Learnable Visual Markers

Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky

Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images

Vladimir Golkov, Marcin J Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox

k*-Nearest Neighbors: From Global to Local

Oren Anava, Kfir Levy

Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition

Theodore Bluche

The Multi-fidelity Multi-armed Bandit

Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff Schneider

Sorting out typicality with the inverse moment matrix SOS polynomial

Edouard Pauwels, Jean B Lasserre

Coresets for Scalable Bayesian Logistic Regression

Jonathan Huggins, Trevor Campbell, Tamara Broderick

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM

Damek Davis, Brent Edmunds, Madeleine Udell

Stochastic Optimization for Large-scale Optimal Transport

Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach

Active Learning with Oracle Epiphany

Tzu-kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu

Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition

Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes

Dimension-Free Iteration Complexity of Finite Sum Optimization Problems

Yossi Arjevani, Ohad Shamir

Spectral Learning of Dynamic Systems from Nonequilibrium Data

Hao Wu, Frank Noe

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J Smola

Cyclades: Conflict-free Asynchronous Machine Learning

Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang

Feature selection in functional data classification with recursive maxima hunting

José L. Torrecilla, Alberto Suárez

CMA-ES with Optimal Covariance Update and Storage Complexity

Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel

Graph Clustering: Block-models and model free results

Yali Wan, Marina Meila

Dynamic Network Surgery for Efficient DNNs

Yiwen Guo, Anbang Yao, Yurong Chen

Greedy Feature Construction

Dino Oglic, Thomas Gärtner

Mapping Estimation for Discrete Optimal Transport

Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard

Sublinear Time Orthogonal Tensor Decomposition

Zhao Song, David Woodruff, Huan Zhang

Incremental Variational Sparse Gaussian Process Regression

Ching-An Cheng, Byron Boots

Split LBI: An Iterative Regularization Path with Structural Sparsity

Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao

Computational and Statistical Tradeoffs in Learning to Rank

Ashish Khetan, Sewoong Oh

Probabilistic Linear Multistep Methods

Onur Teymur, Kostas Zygalakis, Ben Calderhead

Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data

Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi

PAC Reinforcement Learning with Rich Observations

Akshay Krishnamurthy, Alekh Agarwal, John Langford

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz

Learning Tree Structured Potential Games

Vikas Garg, Tommi Jaakkola

Learning shape correspondence with anisotropic convolutional neural networks

Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall

Iterative Refinement of the Approximate Posterior for Directed Belief Networks

Devon Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince Calhoun

Sequential Neural Models with Stochastic Layers

Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther

Optimal Black-Box Reductions Between Optimization Objectives

Zeyuan Allen-Zhu, Elad Hazan

Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods

Antoine Gautier, Quynh N Nguyen, Matthias Hein

Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods

Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

Unsupervised Learning of 3D Structure from Images

Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg

Bootstrap Model Aggregation for Distributed Statistical Learning

JUN HAN, Qiang Liu