NeurIPS 2017

679 papers

Multiscale Quantization for Fast Similarity Search

Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice

An Empirical Bayes Approach to Optimizing Machine Learning Algorithms

James McInerney

From which world is your graph

Cheng Li, Felix MF Wong, Zhenming Liu, Varun Kanade

Optimal Sample Complexity of M-wise Data for Top-K Ranking

Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh

Multi-Objective Non-parametric Sequential Prediction

Guy Uziel, Ran El-Yaniv

Gaussian process based nonlinear latent structure discovery in multivariate spike train data

Anqi Wu, Nicholas A. Roy, Stephen Keeley, Jonathan W Pillow

Tomography of the London Underground: a Scalable Model for Origin-Destination Data

Nicolo Colombo, Ricardo Silva, Soong Moon Kang

Matching on Balanced Nonlinear Representations for Treatment Effects Estimation

Sheng Li, Yun Fu

End-to-end Differentiable Proving

Tim Rocktäschel, Sebastian Riedel

Boltzmann Exploration Done Right

Nicolò Cesa-bianchi, Claudio Gentile, Gabor Lugosi, Gergely Neu

Deep Recurrent Neural Network-Based Identification of Precursor microRNAs

Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

Consistent Multitask Learning with Nonlinear Output Relations

Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil

Spectrally-normalized margin bounds for neural networks

Dylan J Foster, Peter L. Bartlett, Matus J. Telgarsky, Peter L Bartlett, Matus J Telgarsky

Towards Accurate Binary Convolutional Neural Network

Xiaofan Lin, Cong Zhao, Wei Pan

Improving the Expected Improvement Algorithm

Chao Qin, Diego Klabjan, Daniel Russo

Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks

Prateep Bhattacharjee, Sukhendu Das

Multi-Task Learning for Contextual Bandits

Aniket Anand Deshmukh, Urun Dogan, Clay Scott

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Yonatan Belinkov, James Glass

Plan, Attend, Generate: Planning for Sequence-to-Sequence Models

Caglar Gulcehre, Francis Dutil, Adam Trischler, Yoshua Bengio

Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons

Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal

Sparse convolutional coding for neuronal assembly detection

Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization

Pan Xu, Jian Ma, Quanquan Gu

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen

Flexible statistical inference for mechanistic models of neural dynamics

Jan-Matthis Lueckmann, Pedro J Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman

Information-theoretic analysis of generalization capability of learning algorithms

Aolin Xu, Maxim Raginsky

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms

Marco Cusumano-Towner, Vikash K. Mansinghka, Vikash K Mansinghka

Clustering Billions of Reads for DNA Data Storage

Cyrus Rashtchian, Konstantin Makarychev, Miklos Racz, Siena Ang, Djordje Jevdjic

Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System

Chengxu Zhuang, Jonas Kubilius, Mitra JZ Hartmann, Daniel L Yamins

Self-supervised Learning of Motion Capture

Hsiao-Yu Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki

Quantifying how much sensory information in a neural code is relevant for behavior

Giuseppe Pica, Eugenio Piasini, Houman Safaai, Caroline Runyan, Christopher Harvey

Convergence Analysis of Two-layer Neural Networks with ReLU Activation

Yuanzhi Li, Yang Yuan

Matching neural paths: transfer from recognition to correspondence search

Nikolay Savinov, Lubor Ladicky, Marc Pollefeys

Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications

Qinshi Wang, Wei Chen

Label Efficient Learning of Transferable Representations acrosss Domains and Tasks

Zelun Luo, Yuliang Zou, Judy Hoffman, Li F Fei-Fei

Geometric Descent Method for Convex Composite Minimization

Shixiang Chen, Shiqian Ma, Wei Liu

Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang

K-Medoids For K-Means Seeding

James Newling, Francois Fleuret

On Optimal Generalizability in Parametric Learning

Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh

A Sample Complexity Measure with Applications to Learning Optimal Auctions

Vasilis Syrgkanis

On the Complexity of Learning Neural Networks

Le Song, Santosh Vempala, John Wilmes, Bo Xie

Collaborative Deep Learning in Fixed Topology Networks

Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar

Scalable Model Selection for Belief Networks

Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

Xingjian SHI, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung

Monte-Carlo Tree Search by Best Arm Identification

Emilie Kaufmann, Wouter M. Koolen

Train longer, generalize better: closing the generalization gap in large batch training of neural networks

Elad Hoffer, Itay Hubara, Daniel Soudry

Runtime Neural Pruning

Ji Lin, Yongming Rao, Jiwen Lu, Jie Zhou

Inhomogeneous Hypergraph Clustering with Applications

Pan Li, Olgica Milenkovic

Efficient Online Linear Optimization with Approximation Algorithms

Dan Garber

Counterfactual Fairness

Matt J Kusner, Joshua Loftus, Chris Russell, Ricardo Silva

On the Optimization Landscape of Tensor Decompositions

Rong Ge, Tengyu Ma

Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra

John T Halloran, David M Rocke

The Importance of Communities for Learning to Influence

Eric Balkanski, Nicole Immorlica, Yaron Singer

Predicting User Activity Level In Point Processes With Mass Transport Equation

Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song

Parameter-Free Online Learning via Model Selection

Dylan J Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Shixiang (Shane) Gu, Timothy Lillicrap, Richard E Turner, Zoubin Ghahramani, Bernhard Schölkopf

Early stopping for kernel boosting algorithms: A general analysis with localized complexities

Yuting Wei, Fanny Yang, Martin J. Wainwright

Recursive Sampling for the Nystrom Method

Cameron Musco, Christopher Musco

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

Urs Koster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K Bansal

Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization

Yossi Arjevani

Translation Synchronization via Truncated Least Squares

Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang

Adaptive Active Hypothesis Testing under Limited Information

Fabio Cecchi, Nidhi Hegde

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell

Imagination-Augmented Agents for Deep Reinforcement Learning

Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez

Towards Generalization and Simplicity in Continuous Control

Aravind Rajeswaran, Kendall Lowrey, Emanuel V. Todorov, Sham M. Kakade

Certified Defenses for Data Poisoning Attacks

Jacob Steinhardt, Pang Wei W Koh, Percy Liang

GP CaKe: Effective brain connectivity with causal kernels

Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Siddharth N, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman

On Blackbox Backpropagation and Jacobian Sensing

Krzysztof M Choromanski, Vikas Sindhwani

Learning a Multi-View Stereo Machine

Abhishek Kar, Christian Häne, Jitendra Malik

Scalable Demand-Aware Recommendation

Jinfeng Yi, Cho-jui Hsieh, Kush R Varshney, Lijun Zhang, Yao Li

Optimized Pre-Processing for Discrimination Prevention

Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R Varshney

Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network

Lixin Fan

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization

Jeffrey Regier, Jon McAuliffe, Michael I. Jordan, Michael I Jordan

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces

Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D Simeral, Beata Jarosiewicz

Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search

Mohammad Ali Bashiri, Xinhua Zhang

Bandits Dueling on Partially Ordered Sets

Julien Audiffren, Liva Ralaivola

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models

Chris Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark Girolami

Scalable Planning with Tensorflow for Hybrid Nonlinear Domains

Ga Wu, Buser Say, Scott Sanner

Rotting Bandits

Nir Levine, Koby Crammer, Shie Mannor

A Linear-Time Kernel Goodness-of-Fit Test

Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton

Fair Clustering Through Fairlets

Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

Scalable Log Determinants for Gaussian Process Kernel Learning

Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G Wilson

Ranking Data with Continuous Labels through Oriented Recursive Partitions

Stéphan Clémençon, Mastane Achab

A Decomposition of Forecast Error in Prediction Markets

Miro Dudik, Sebastien Lahaie, Ryan M Rogers, Jennifer Wortman Vaughan

Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

Emmanouil Platanios, Hoifung Poon, Tom M. Mitchell, Eric J Horvitz

Causal Effect Inference with Deep Latent-Variable Models

Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel

Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions

Aryeh Kontorovich, Sivan Sabato, Roi Weiss

An Empirical Study on The Properties of Random Bases for Kernel Methods

Maximilian Alber, Pieter-jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha

Online Learning for Multivariate Hawkes Processes

Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash

Conic Scan-and-Cover algorithms for nonparametric topic modeling

Mikhail Yurochkin, Aritra Guha, Xuanlong Nguyen

Learning Neural Representations of Human Cognition across Many fMRI Studies

Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gael Varoquaux

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity

Asish Ghoshal, Jean Honorio

Regret Minimization in MDPs with Options without Prior Knowledge

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill

Z-Forcing: Training Stochastic Recurrent Networks

Anirudh Goyal ALIAS PARTH GOYAL, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio

Subset Selection and Summarization in Sequential Data

Ehsan Elhamifar, M. Clara De Paolis Kaluza

Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications

Linus Hamilton, Frederic Koehler, Ankur Moitra

Associative Embedding: End-to-End Learning for Joint Detection and Grouping

Alejandro Newell, Zhiao Huang, Jia Deng

Teaching Machines to Describe Images with Natural Language Feedback

huan ling, Sanja Fidler

Gauging Variational Inference

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