NeurIPS 2017

679 papers

Detrended Partial Cross Correlation for Brain Connectivity Analysis

Jaime Ide, Fábio Cappabianco, Fabio Faria, Chiang-shan R Li

Value Prediction Network

Junhyuk Oh, Satinder Singh, Honglak Lee

A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis

Tor Lattimore

NeuralFDR: Learning Discovery Thresholds from Hypothesis Features

Fei Xia, Martin J Zhang, James Y Zou, David Tse

Generating steganographic images via adversarial training

Jamie Hayes, George Danezis

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities

Arun Suggala, Mladen Kolar, Pradeep K Ravikumar

Multi-way Interacting Regression via Factorization Machines

Mikhail Yurochkin, Xuanlong Nguyen, nikolaos Vasiloglou

Nonlinear Acceleration of Stochastic Algorithms

Damien Scieur, Francis Bach, Alexandre D'aspremont

Balancing information exposure in social networks

Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti

Active Exploration for Learning Symbolic Representations

Garrett Andersen, George Konidaris

Natural Value Approximators: Learning when to Trust Past Estimates

Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver

EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms

Yogatheesan Varatharajah, Min Jin Chong, Krishnakant Saboo, Brent Berry, Benjamin Brinkmann

Asynchronous Coordinate Descent under More Realistic Assumptions

Tao Sun, Robert Hannah, Wotao Yin

Bridging the Gap Between Value and Policy Based Reinforcement Learning

Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans

Cold-Start Reinforcement Learning with Softmax Policy Gradient

Nan Ding, Radu Soricut

Filtering Variational Objectives

Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi

Parametric Simplex Method for Sparse Learning

Haotian Pang, Han Liu, Robert J Vanderbei, Tuo Zhao

Language Modeling with Recurrent Highway Hypernetworks

Joseph Suarez

The Reversible Residual Network: Backpropagation Without Storing Activations

Aidan N Gomez, Mengye Ren, Raquel Urtasun, Roger B Grosse

Variational Inference for Gaussian Process Models with Linear Complexity

Ching-An Cheng, Byron Boots

Gradient Episodic Memory for Continual Learning

David Lopez-paz, Marc'aurelio Ranzato

Universal consistency and minimax rates for online Mondrian Forests

Jaouad Mourtada, Stephane Gaiffas, Erwan Scornet

Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit

Laurence Aitchison, Lloyd Russell, Adam M Packer, Jinyao Yan, Philippe Castonguay

Dual Path Networks

Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan

Gradient Descent Can Take Exponential Time to Escape Saddle Points

Simon S Du, Chi Jin, Jason Lee, Aarti Singh, Barnabás Póczos

Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian, Ce Zhang, Huan Zhang, Cho-jui Hsieh, Wei Zhang

Overcoming Catastrophic Forgetting by Incremental Moment Matching

Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-tak Zhang

Generative Local Metric Learning for Kernel Regression

Yung-kyun Noh, Masashi Sugiyama, Kee-eung Kim, Frank Park, Daniel D. Lee

Multimodal Learning and Reasoning for Visual Question Answering

Ilija Ilievski, Jiashi Feng

Alternating Estimation for Structured High-Dimensional Multi-Response Models

Sheng Chen, Arindam Banerjee

Avoiding Discrimination through Causal Reasoning

Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing

Convergence rates of a partition based Bayesian multivariate density estimation method

Linxi Liu, Dangna Li, Wing Hung Wong

Protein Interface Prediction using Graph Convolutional Networks

Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur

Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent

Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer

Wasserstein Learning of Deep Generative Point Process Models

Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song

On the Consistency of Quick Shift

Heinrich Jiang

Shape and Material from Sound

Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum

Unbounded cache model for online language modeling with open vocabulary

Edouard Grave, Moustapha M Cisse, Armand Joulin

Differentially Private Empirical Risk Minimization Revisited: Faster and More General

Di Wang, Minwei Ye, Jinhui Xu

Streaming Sparse Gaussian Process Approximations

Thang D Bui, Cuong Nguyen, Richard E Turner

Preventing Gradient Explosions in Gated Recurrent Units

Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura

Learning to Compose Domain-Specific Transformations for Data Augmentation

Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Re

Learning Mixture of Gaussians with Streaming Data

Aditi Raghunathan, Prateek Jain, Ravishankar Krishnawamy

Optimal Shrinkage of Singular Values Under Random Data Contamination

Danny Barash, Matan Gavish

Deep Sets

Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov

Unsupervised Sequence Classification using Sequential Output Statistics

Yu Liu, Jianshu Chen, Li Deng

Learning Low-Dimensional Metrics

Blake Mason, Lalit Jain, Robert Nowak

Hash Embeddings for Efficient Word Representations

Dan Tito Svenstrup, Jonas Hansen, Ole Winther

Interactive Submodular Bandit

Lin Chen, Andreas Krause, Amin Karbasi

A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control

Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright

An inner-loop free solution to inverse problems using deep neural networks

Kai Fan, Qi Wei, Lawrence Carin, Katherine A. Heller

Reconstructing perceived faces from brain activations with deep adversarial neural decoding

Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier

Estimating Mutual Information for Discrete-Continuous Mixtures

Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

On the Model Shrinkage Effect of Gamma Process Edge Partition Models

Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura

Tensor Biclustering

Soheil Feizi, Hamid Javadi, David Tse

Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues

Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, Jascha Sohl-dickstein

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting

Rebecca Morrison, Ricardo Baptista, Youssef Marzouk

Adaptive Batch Size for Safe Policy Gradients

Matteo Papini, Matteo Pirotta, Marcello Restelli

Trimmed Density Ratio Estimation

Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

Simple strategies for recovering inner products from coarsely quantized random projections

Ping Li, Martin Slawski

A Learning Error Analysis for Structured Prediction with Approximate Inference

Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization

Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

Approximation Algorithms for ℓ_0-Low Rank Approximation

Karl Bringmann, Pavel Kolev, David Woodruff

Deep Supervised Discrete Hashing

Qi Li, Zhenan Sun, Ran He, Tieniu Tan

QMDP-Net: Deep Learning for Planning under Partial Observability

Peter Karkus, David Hsu, Wee Sun Lee

Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos

Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud

Hierarchical Clustering Beyond the Worst-Case

Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

Incorporating Side Information by Adaptive Convolution

Di Kang, Debarun Dhar, Antoni Chan

Learning multiple visual domains with residual adapters

Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi

Scalable Variational Inference for Dynamical Systems

Nico S Gorbach, Stefan Bauer, Joachim M Buhmann

Consistent Robust Regression

Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar

Compatible Reward Inverse Reinforcement Learning

Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli

From Bayesian Sparsity to Gated Recurrent Nets

Hao He, Bo Xin, Satoshi Ikehata, David Wipf

Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication

Qian Yu, Mohammad Maddah-Ali, Salman Avestimehr

Stochastic Mirror Descent in Variationally Coherent Optimization Problems

Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, Peter W. Glynn

Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks

Arash Vahdat

A simple neural network module for relational reasoning

Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu

Multitask Spectral Learning of Weighted Automata

Guillaume Rabusseau, Borja Balle, Joelle Pineau

Beyond Parity: Fairness Objectives for Collaborative Filtering

Sirui Yao, Bert Huang

Dual Discriminator Generative Adversarial Nets

Tu Nguyen, Trung Le, Hung Vu, Dinh Phung

PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S Yu

Fast-Slow Recurrent Neural Networks

Asier Mujika, Florian Meier, Angelika Steger

Robust Estimation of Neural Signals in Calcium Imaging

Hakan Inan, Murat A Erdogdu, Mark Schnitzer

Deep Subspace Clustering Networks

Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid

Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding

Wenbing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun

Learning Chordal Markov Networks via Branch and Bound

Kari Rantanen, Antti Hyttinen, Matti Järvisalo

Learning Efficient Object Detection Models with Knowledge Distillation

Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker

Minimal Exploration in Structured Stochastic Bandits

Richard Combes, Stefan Magureanu, Alexandre Proutiere

Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs

Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer

Noise-Tolerant Interactive Learning Using Pairwise Comparisons

Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski

Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems

Celestine Dünner, Thomas Parnell, Martin Jaggi

Is the Bellman residual a bad proxy?

Matthieu Geist, Bilal Piot, Olivier Pietquin

Collapsed variational Bayes for Markov jump processes

Boqian Zhang, Jiangwei Pan, Vinayak A Rao

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

Matthias Hein, Maksym Andriushchenko

Learning Graph Representations with Embedding Propagation

Mathias Niepert, Alberto Garcia-Duran, Alberto Garcia Duran

Learning ReLUs via Gradient Descent

Mahdi Soltanolkotabi

Efficient and Flexible Inference for Stochastic Systems

Stefan Bauer, Nico S Gorbach, Djordje Miladinovic, Joachim M Buhmann

Bayesian Dyadic Trees and Histograms for Regression

Stéphanie van der Pas, Veronika Ročková