NeurIPS 2017

679 papers

Inference in Graphical Models via Semidefinite Programming Hierarchies

Murat A Erdogdu, Yash Deshpande, Andrea Montanari

Repeated Inverse Reinforcement Learning

Kareem Amin, Nan Jiang, Satinder Singh

Visual Interaction Networks: Learning a Physics Simulator from Video

Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu

Controllable Invariance through Adversarial Feature Learning

Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig

Learning Active Learning from Data

Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

Neural system identification for large populations separating “what” and “where”

David Klindt, Alexander S Ecker, Thomas Euler, Matthias Bethge

Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach

Roel Dobbe, David Fridovich-Keil, Claire Tomlin

Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls

Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li

Nonbacktracking Bounds on the Influence in Independent Cascade Models

Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee

A Unified Approach to Interpreting Model Predictions

Scott M Lundberg, Su-in Lee

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter

TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems

Simon S Du, Yining Wang, Aarti Singh

Matrix Norm Estimation from a Few Entries

Ashish Khetan, Sewoong Oh

Adaptive stimulus selection for optimizing neural population responses

Benjamin Cowley, Ryan Williamson, Katerina Clemens, Matthew Smith, Byron M. Yu

Improved Training of Wasserstein GANs

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron C. Courville

Mapping distinct timescales of functional interactions among brain networks

Mali Sundaresan, Arshed Nabeel, Devarajan Sridharan

Revenue Optimization with Approximate Bid Predictions

Andres Munoz, Sergei Vassilvitskii

Deep Learning with Topological Signatures

Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl

Triple Generative Adversarial Nets

Chongxuan Li, Taufik Xu, Jun Zhu, Bo Zhang

Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM

Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations

Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte

Unsupervised Transformation Learning via Convex Relaxations

Tatsunori B Hashimoto, Percy Liang, John C. Duchi

Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks

Learned D-AMP: Principled Neural Network based Compressive Image Recovery

Chris Metzler, Ali Mousavi, Richard Baraniuk

Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem

Yasin Abbasi Yadkori, Victor Gabillon, Peter L. Bartlett, Peter L Bartlett

Multiresolution Kernel Approximation for Gaussian Process Regression

Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler

Concrete Dropout

Yarin Gal, Jiri Hron, Alex Kendall

Reinforcement Learning under Model Mismatch

Aurko Roy, Huan Xu, Sebastian Pokutta

Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex

Chaobing Song, Shaobo Cui, Yong Jiang, Shu-Tao Xia

Pixels to Graphs by Associative Embedding

Alejandro Newell, Jia Deng

Online Learning with Transductive Regret

Mehryar Mohri, Scott Yang

Lookahead Bayesian Optimization with Inequality Constraints

Remi Lam, Karen Willcox

Hiding Images in Plain Sight: Deep Steganography

Shumeet Baluja

Working hard to know your neighbor's margins: Local descriptor learning loss

Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas

Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure

Alberto Bietti, Julien Mairal

Nonparametric Online Regression while Learning the Metric

Ilja Kuzborskij, Nicolò Cesa-bianchi

From Parity to Preference-based Notions of Fairness in Classification

Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, Adrian Weller

Doubly Stochastic Variational Inference for Deep Gaussian Processes

Hugh Salimbeni, Marc Deisenroth

First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization

Aryan Mokhtari, Alejandro Ribeiro

Non-convex Finite-Sum Optimization Via SCSG Methods

Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan, Michael I Jordan

Mean Field Residual Networks: On the Edge of Chaos

Ge Yang, Samuel Schoenholz

The Marginal Value of Adaptive Gradient Methods in Machine Learning

Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht

Toward Multimodal Image-to-Image Translation

Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer

A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent

Ben London

Safe and Nested Subgame Solving for Imperfect-Information Games

Noam Brown, Tuomas Sandholm

Online control of the false discovery rate with decaying memory

Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan, Michael I Jordan

Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models

Rishit Sheth, Roni Khardon

PixelGAN Autoencoders

Alireza Makhzani, Brendan J. Frey

Group Sparse Additive Machine

Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang

Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers

Cong Fang, Feng Cheng, Zhouchen Lin

Gradient descent GAN optimization is locally stable

Vaishnavh Nagarajan, J. Zico Kolter

Positive-Unlabeled Learning with Non-Negative Risk Estimator

Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, Masashi Sugiyama

Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation

Christian Borgs, Jennifer Chayes, Christina E. Lee, Devavrat Shah

Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis

Jian Zhao, Lin Xiong, Panasonic Karlekar Jayashree, Jianshu Li, Fang Zhao

Training Deep Networks without Learning Rates Through Coin Betting

Francesco Orabona, Tatiana Tommasi

Stabilizing Training of Generative Adversarial Networks through Regularization

Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther

Probabilistic Rule Realization and Selection

Haizi Yu, Tianxi Li, Lav R. Varshney

Neural Discrete Representation Learning

Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu

Online Dynamic Programming

Holakou Rahmanian, Manfred K. Warmuth

Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes

Anton Mallasto, Aasa Feragen

Targeting EEG/LFP Synchrony with Neural Nets

Yitong Li, michael Murias, samantha Major, geraldine Dawson, Kafui Dzirasa

Good Semi-supervised Learning That Requires a Bad GAN

Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat

Context Selection for Embedding Models

Liping Liu, Francisco Ruiz, Susan Athey, David Blei

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling

Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing

Safe Model-based Reinforcement Learning with Stability Guarantees

Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, Andreas Krause

Learning with Feature Evolvable Streams

Lijun Zhang, Zhi-Hua Zhou, Bojian Hou, Bo-Jian Hou

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization

Tomoya Murata, Taiji Suzuki

Sobolev Training for Neural Networks

Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, Razvan Pascanu

Hierarchical Attentive Recurrent Tracking

Adam Kosiorek, Alex Bewley, Ingmar Posner

VAIN: Attentional Multi-agent Predictive Modeling

Yedid Hoshen

Communication-Efficient Distributed Learning of Discrete Distributions

Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak

A Minimax Optimal Algorithm for Crowdsourcing

Thomas Bonald, Richard Combes

A Screening Rule for l1-Regularized Ising Model Estimation

Zhaobin Kuang, Sinong Geng, David Page

Predicting Scene Parsing and Motion Dynamics in the Future

Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin

Generalizing GANs: A Turing Perspective

Roderich Gross, Yue Gu, Wei Li, Melvin Gauci

On Frank-Wolfe and Equilibrium Computation

Jacob D. Abernethy, Jun-Kun Wang

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities

Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stephane Mallat

Linearly constrained Gaussian processes

Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B Schön

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

Ahmet Alacaoglu, Quoc Tran Dinh, Olivier Fercoq, Volkan Cevher

Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback

Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani

Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning

Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He

Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols

Serhii Havrylov, Ivan Titov

Discriminative State Space Models

Vitaly Kuznetsov, Mehryar Mohri

Modulating early visual processing by language

Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding

Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Dustin Tran, Rajesh Ranganath, David Blei

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds

Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao

Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga

Label Distribution Learning Forests

Wei Shen, KAI ZHAO, Yilu Guo, Alan L. Yuille

Sparse Approximate Conic Hulls

Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi

Max-Margin Invariant Features from Transformed Unlabelled Data

Dipan Pal, Ashwin Kannan, Gautam Arakalgud, Marios Savvides

MaskRNN: Instance Level Video Object Segmentation

Yuan-Ting Hu, Jia-Bin Huang, Alexander Schwing

Learning with Average Top-k Loss

Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-gang Hu, Baogang Hu

Deanonymization in the Bitcoin P2P Network

Giulia Fanti, Pramod Viswanath

Learning Hierarchical Information Flow with Recurrent Neural Modules

Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess