Advances in Neural Information Processing Systems, NIPS 2016


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[?] A research artifact is any by-product of a research project that is not directly included in the published research paper. In Computer Science research this is often source code and data sets, but it could also be media, documentation, inputs to proof assistants, shell-scripts to run experiments, etc.
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Dialog-based Language Learning

Jason Weston

Dialog-based Language Learning

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Linear Contextual Bandits with Knapsacks

Shipra Agrawal, Nikhil R. Devanur

Linear Contextual Bandits with Knapsacks

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

Amit Daniely, Roy Frostig, Yoram Singer

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

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Interpretable Nonlinear Dynamic Modeling of Neural Trajectories

Yuan Zhao, Il Memming Park

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories

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Deep ADMM-Net for Compressive Sensing MRI

Yan Yang, Jian Sun, Huibin Li, Zongben Xu

Deep ADMM-Net for Compressive Sensing MRI

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Optimal Sparse Linear Encoders and Sparse PCA

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Encoders and Sparse PCA

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A scaled Bregman theorem with applications

Richard Nock, Aditya Krishna Menon, Cheng Soon Ong

A scaled Bregman theorem with applications

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The Power of Optimization from Samples

Eric Balkanski, Aviad Rubinstein, Yaron Singer

The Power of Optimization from Samples

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Stochastic Structured Prediction under Bandit Feedback

Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo

Stochastic Structured Prediction under Bandit Feedback

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Probabilistic Linear Multistep Methods

Onur Teymur, Konstantinos Zygalakis, Ben Calderhead

Probabilistic Linear Multistep Methods

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Learning Tree Structured Potential Games

Vikas K. Garg, Tommi S. Jaakkola

Learning Tree Structured Potential Games

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Sampling for Bayesian Program Learning

Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum

Sampling for Bayesian Program Learning

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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Qiang Liu, Dilin Wang

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

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Improved Error Bounds for Tree Representations of Metric Spaces

Samir Chowdhury, Facundo Mémoli, Zane T. Smith

Improved Error Bounds for Tree Representations of Metric Spaces

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Variance Reduction in Stochastic Gradient Langevin Dynamics

Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing

Variance Reduction in Stochastic Gradient Langevin Dynamics

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Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint

Nguyen Cuong, Huan Xu

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint

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Multivariate tests of association based on univariate tests

Ruth Heller, Yair Heller

Multivariate tests of association based on univariate tests

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

Antoine Gautier, Quynh N. Nguyen, Matthias Hein

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

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Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy

Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy

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Online Convex Optimization with Unconstrained Domains and Losses

Ashok Cutkosky, Kwabena A. Boahen

Online Convex Optimization with Unconstrained Domains and Losses

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Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

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Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

Hongseok Namkoong, John C. Duchi

Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

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Full-Capacity Unitary Recurrent Neural Networks

Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les E. Atlas

Full-Capacity Unitary Recurrent Neural Networks

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

Reza Eghbali, Maryam Fazel

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

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Even Faster SVD Decomposition Yet Without Agonizing Pain

Zeyuan Allen Zhu, Yuanzhi Li

Even Faster SVD Decomposition Yet Without Agonizing Pain

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Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

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The Multiscale Laplacian Graph Kernel

Risi Kondor, Horace Pan

The Multiscale Laplacian Graph Kernel

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Backprop KF: Learning Discriminative Deterministic State Estimators

Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel

Backprop KF: Learning Discriminative Deterministic State Estimators

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Tensor Switching Networks

Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox

Tensor Switching Networks

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PAC-Bayesian Theory Meets Bayesian Inference

Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien

PAC-Bayesian Theory Meets Bayesian Inference

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Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov

Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

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Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods

Andrej Risteski, Yuanzhi Li

Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods

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Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis

Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis

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Architectural Complexity Measures of Recurrent Neural Networks

Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio

Architectural Complexity Measures of Recurrent Neural Networks

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Flexible Models for Microclustering with Application to Entity Resolution

Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna M. Wallach, Abbas Zaidi, Beka Steorts

Flexible Models for Microclustering with Application to Entity Resolution

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Catching heuristics are optimal control policies

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

Catching heuristics are optimal control policies

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Deep Learning without Poor Local Minima

Kenji Kawaguchi

Deep Learning without Poor Local Minima

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

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

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

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Edge-exchangeable graphs and sparsity

Diana Cai, Trevor Campbell, Tamara Broderick

Edge-exchangeable graphs and sparsity

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Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong

Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

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Stochastic Optimization for Large-scale Optimal Transport

Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach

Stochastic Optimization for Large-scale Optimal Transport

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Unsupervised Domain Adaptation with Residual Transfer Networks

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

Unsupervised Domain Adaptation with Residual Transfer Networks

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LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Xiang Li, Tao Qin, Jian Yang, Xiaolin Hu, Tie-Yan Liu

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

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Learning Bayesian networks with ancestral constraints

Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche

Learning Bayesian networks with ancestral constraints

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A Bayesian method for reducing bias in neural representational similarity analysis

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

A Bayesian method for reducing bias in neural representational similarity analysis

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An Efficient Streaming Algorithm for the Submodular Cover Problem

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

An Efficient Streaming Algorithm for the Submodular Cover Problem

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Sublinear Time Orthogonal Tensor Decomposition

Zhao Song, David P. Woodruff, Huan Zhang

Sublinear Time Orthogonal Tensor Decomposition

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

Lev Bogolubsky, Pavel Dvurechensky, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii

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

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Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula

Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová

Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula

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Dynamic matrix recovery from incomplete observations under an exact low-rank constraint

Liangbei Xu, Mark A. Davenport

Dynamic matrix recovery from incomplete observations under an exact low-rank constraint

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Finding significant combinations of features in the presence of categorical covariates

Laetitia Papaxanthos, Felipe Llinares-López, Dean A. Bodenham, Karsten M. Borgwardt

Finding significant combinations of features in the presence of categorical covariates

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Long-term Causal Effects via Behavioral Game Theory

Panagiotis Toulis, David C. Parkes

Long-term Causal Effects via Behavioral Game Theory

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Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm

Kejun Huang, Xiao Fu, Nikos D. Sidiropoulos

Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm

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On Mixtures of Markov Chains

Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii

On Mixtures of Markov Chains

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Composing graphical models with neural networks for structured representations and fast inference

Matthew J. Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, Sandeep R. Datta

Composing graphical models with neural networks for structured representations and fast inference

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Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

Matteo Turchetta, Felix Berkenkamp, Andreas Krause

Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

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Data driven estimation of Laplace-Beltrami operator

Frédéric Chazal, Ilaria Giulini, Bertrand Michel

Data driven estimation of Laplace-Beltrami operator

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Statistical Inference for Pairwise Graphical Models Using Score Matching

Ming Yu, Mladen Kolar, Varun Gupta

Statistical Inference for Pairwise Graphical Models Using Score Matching

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Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain

Timothy N. Rubin, Oluwasanmi Koyejo, Michael N. Jones, Tal Yarkoni

Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain

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Optimistic Bandit Convex Optimization

Scott Yang, Mehryar Mohri

Optimistic Bandit Convex Optimization

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Probing the Compositionality of Intuitive Functions

Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J. Gershman

Probing the Compositionality of Intuitive Functions

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Adversarial Multiclass Classification: A Risk Minimization Perspective

Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart

Adversarial Multiclass Classification: A Risk Minimization Perspective

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

Nicolas Boumal, Vladislav Voroninski, Afonso S. Bandeira

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

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Clustering Signed Networks with the Geometric Mean of Laplacians

Pedro Mercado, Francesco Tudisco, Matthias Hein

Clustering Signed Networks with the Geometric Mean of Laplacians

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Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning

Wouter M. Koolen, Peter Grünwald, Tim van Erven

Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning

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A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization

Jingwei Liang, Jalal Fadili, Gabriel Peyré

A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization

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Data Poisoning Attacks on Factorization-Based Collaborative Filtering

Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik

Data Poisoning Attacks on Factorization-Based Collaborative Filtering

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Unsupervised Learning of Spoken Language with Visual Context

David F. Harwath, Antonio Torralba, James R. Glass

Unsupervised Learning of Spoken Language with Visual Context

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Adaptive Skills Adaptive Partitions (ASAP)

Daniel J. Mankowitz, Timothy Arthur Mann, Shie Mannor

Adaptive Skills Adaptive Partitions (ASAP)

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High resolution neural connectivity from incomplete tracing data using nonnegative spline regression

Kameron D. Harris, Stefan Mihalas, Eric Shea-Brown

High resolution neural connectivity from incomplete tracing data using nonnegative spline regression

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Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Andreas Veit, Michael J. Wilber, Serge J. Belongie

Residual Networks Behave Like Ensembles of Relatively Shallow Networks

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Error Analysis of Generalized Nyström Kernel Regression

Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai

Error Analysis of Generalized Nyström Kernel Regression

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Select-and-Sample for Spike-and-Slab Sparse Coding

Abdul-Saboor Sheikh, Jörg Lücke

Select-and-Sample for Spike-and-Slab Sparse Coding

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End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

Julien Mairal

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

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Relevant sparse codes with variational information bottleneck

Matthew Chalk, Olivier Marre, Gasper Tkacik

Relevant sparse codes with variational information bottleneck

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Single-Image Depth Perception in the Wild

Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng

Single-Image Depth Perception in the Wild

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DECOrrelated feature space partitioning for distributed sparse regression

Xiangyu Wang, David B. Dunson, Chenlei Leng

DECOrrelated feature space partitioning for distributed sparse regression

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The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

Jian Wu, Peter I. Frazier

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

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Stochastic Online AUC Maximization

Yiming Ying, Longyin Wen, Siwei Lyu

Stochastic Online AUC Maximization

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Crowdsourced Clustering: Querying Edges vs Triangles

Ramya Korlakai Vinayak, Babak Hassibi

Crowdsourced Clustering: Querying Edges vs Triangles

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Data Programming: Creating Large Training Sets, Quickly

Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré

Data Programming: Creating Large Training Sets, Quickly

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Human Decision-Making under Limited Time

Pedro A. Ortega, Alan A. Stocker

Human Decision-Making under Limited Time

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A Communication-Efficient Parallel Algorithm for Decision Tree

Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu

A Communication-Efficient Parallel Algorithm for Decision Tree

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Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

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Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Tianfan Xue, Jiajun Wu, Katherine L. Bouman, Bill Freeman

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

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Visual Question Answering with Question Representation Update (QRU)

Ruiyu Li, Jiaya Jia

Visual Question Answering with Question Representation Update (QRU)

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Dual Space Gradient Descent for Online Learning

Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung

Dual Space Gradient Descent for Online Learning

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Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much

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Multistage Campaigning in Social Networks

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

Multistage Campaigning in Social Networks

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Constraints Based Convex Belief Propagation

Yaniv Tenzer, Alexander G. Schwing, Kevin Gimpel, Tamir Hazan

Constraints Based Convex Belief Propagation

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Dense Associative Memory for Pattern Recognition

Dmitry Krotov, John J. Hopfield

Dense Associative Memory for Pattern Recognition

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Neural Universal Discrete Denoiser

Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon

Neural Universal Discrete Denoiser

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

Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sébastien Le Digabel

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

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An equivalence between high dimensional Bayes optimal inference and M-estimation

Madhu Advani, Surya Ganguli

An equivalence between high dimensional Bayes optimal inference and M-estimation

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Reward Augmented Maximum Likelihood for Neural Structured Prediction

Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans

Reward Augmented Maximum Likelihood for Neural Structured Prediction

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Efficient state-space modularization for planning: theory, behavioral and neural signatures

Daniel McNamee, Daniel M. Wolpert, Máté Lengyel

Efficient state-space modularization for planning: theory, behavioral and neural signatures

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Universal Correspondence Network

Christopher Bongsoo Choy, JunYoung Gwak, Silvio Savarese, Manmohan Krishna Chandraker

Universal Correspondence Network

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Orthogonal Random Features

Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar

Orthogonal Random Features

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Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

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Swapout: Learning an ensemble of deep architectures

Saurabh Singh, Derek Hoiem, David A. Forsyth

Swapout: Learning an ensemble of deep architectures

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A Credit Assignment Compiler for Joint Prediction

Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford

A Credit Assignment Compiler for Joint Prediction

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Active Learning from Imperfect Labelers

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Active Learning from Imperfect Labelers

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Learning What and Where to Draw

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

Learning What and Where to Draw

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Hierarchical Question-Image Co-Attention for Visual Question Answering

Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh

Hierarchical Question-Image Co-Attention for Visual Question Answering

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Learning Infinite RBMs with Frank-Wolfe

Wei Ping, Qiang Liu, Alexander T. Ihler

Learning Infinite RBMs with Frank-Wolfe

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Optimistic Gittins Indices

Eli Gutin, Vivek F. Farias

Optimistic Gittins Indices

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Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis

Yoshinobu Kawahara

Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis

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

Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover

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

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On Valid Optimal Assignment Kernels and Applications to Graph Classification

Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson

On Valid Optimal Assignment Kernels and Applications to Graph Classification

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Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

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Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning

Jean-Bastien Grill, Michal Valko, Rémi Munos

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning

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Tractable Operations for Arithmetic Circuits of Probabilistic Models

Yujia Shen, Arthur Choi, Adnan Darwiche

Tractable Operations for Arithmetic Circuits of Probabilistic Models

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Learning Deep Parsimonious Representations

Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun

Learning Deep Parsimonious Representations

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A Probabilistic Programming Approach To Probabilistic Data Analysis

Feras Saad, Vikash K. Mansinghka

A Probabilistic Programming Approach To Probabilistic Data Analysis

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Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA

Aapo Hyvärinen, Hiroshi Morioka

Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA

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Feature selection in functional data classification with recursive maxima hunting

José L. Torrecilla, Alberto Suárez

Feature selection in functional data classification with recursive maxima hunting

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Privacy Odometers and Filters: Pay-as-you-Go Composition

Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan Ullman

Privacy Odometers and Filters: Pay-as-you-Go Composition

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VIME: Variational Information Maximizing Exploration

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

VIME: Variational Information Maximizing Exploration

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A state-space model of cross-region dynamic connectivity in MEG/EEG

Ying Yang, Elissa Aminoff, Michael J. Tarr, Robert E. Kass

A state-space model of cross-region dynamic connectivity in MEG/EEG

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"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation

Wenhao Zhang, He Wang, K. Y. Michael Wong, Si Wu

"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation

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Multiple-Play Bandits in the Position-Based Model

Paul Lagrée, Claire Vernade, Olivier Cappé

Multiple-Play Bandits in the Position-Based Model

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DISCO Nets : DISsimilarity COefficients Networks

Diane Bouchacourt, Pawan Kumar Mudigonda, Sebastian Nowozin

DISCO Nets : DISsimilarity COefficients Networks

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Efficient Nonparametric Smoothness Estimation

Shashank Singh, Simon S. Du, Barnabás Póczos

Efficient Nonparametric Smoothness Estimation

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Coupled Generative Adversarial Networks

Ming-Yu Liu, Oncel Tuzel

Coupled Generative Adversarial Networks

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Graph Clustering: Block-models and model free results

Yali Wan, Marina Meila

Graph Clustering: Block-models and model free results

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Without-Replacement Sampling for Stochastic Gradient Methods

Ohad Shamir

Without-Replacement Sampling for Stochastic Gradient Methods

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A Locally Adaptive Normal Distribution

Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg

A Locally Adaptive Normal Distribution

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A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

Ming Lin, Jieping Ye

A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

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Interaction Networks for Learning about Objects, Relations and Physics

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

Interaction Networks for Learning about Objects, Relations and Physics

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A Bandit Framework for Strategic Regression

Yang Liu, Yiling Chen

A Bandit Framework for Strategic Regression

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Showing versus doing: Teaching by demonstration

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

Showing versus doing: Teaching by demonstration

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Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions

Yichen Wang, Nan Du, Rakshit Trivedi, Le Song

Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions

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Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

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End-to-End Goal-Driven Web Navigation

Rodrigo Nogueira, Kyunghyun Cho

End-to-End Goal-Driven Web Navigation

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Efficient Second Order Online Learning by Sketching

Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford

Efficient Second Order Online Learning by Sketching

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

Guillaume Papa, Aurélien Bellet, Stéphan Clémençon

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

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Threshold Learning for Optimal Decision Making

Nathan F. Lepora

Threshold Learning for Optimal Decision Making

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Learning Multiagent Communication with Backpropagation

Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus

Learning Multiagent Communication with Backpropagation

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Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

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

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

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Learning to learn by gradient descent by gradient descent

Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas

Learning to learn by gradient descent by gradient descent

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Review Networks for Caption Generation

Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan Salakhutdinov

Review Networks for Caption Generation

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Active Learning with Oracle Epiphany

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

Active Learning with Oracle Epiphany

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Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models

Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel

Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models

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Optimal Black-Box Reductions Between Optimization Objectives

Zeyuan Allen Zhu, Elad Hazan

Optimal Black-Box Reductions Between Optimization Objectives

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Fairness in Learning: Classic and Contextual Bandits

Matthew Joseph, Michael J. Kearns, Jamie H. Morgenstern, Aaron Roth

Fairness in Learning: Classic and Contextual Bandits

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Variational Information Maximization for Feature Selection

Shuyang Gao, Greg Ver Steeg, Aram Galstyan

Variational Information Maximization for Feature Selection

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Optimizing affinity-based binary hashing using auxiliary coordinates

Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán

Optimizing affinity-based binary hashing using auxiliary coordinates

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k*-Nearest Neighbors: From Global to Local

Oren Anava, Kfir Y. Levy

k*-Nearest Neighbors: From Global to Local

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Synthesis of MCMC and Belief Propagation

Sungsoo Ahn, Michael Chertkov, Jinwoo Shin

Synthesis of MCMC and Belief Propagation

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Consistent Kernel Mean Estimation for Functions of Random Variables

Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf

Consistent Kernel Mean Estimation for Functions of Random Variables

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An urn model for majority voting in classification ensembles

Víctor Soto, Alberto Suárez, Gonzalo Martínez-Muñoz

An urn model for majority voting in classification ensembles

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Budgeted stream-based active learning via adaptive submodular maximization

Kaito Fujii, Hisashi Kashima

Budgeted stream-based active learning via adaptive submodular maximization

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Coordinate-wise Power Method

Qi Lei, Kai Zhong, Inderjit S. Dhillon

Coordinate-wise Power Method

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Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers

Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers

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Launch and Iterate: Reducing Prediction Churn

Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta

Launch and Iterate: Reducing Prediction Churn

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Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes

Dan Garber, Ofer Meshi

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes

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Cooperative Graphical Models

Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause

Cooperative Graphical Models

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Disentangling factors of variation in deep representation using adversarial training

Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun

Disentangling factors of variation in deep representation using adversarial training

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R-FCN: Object Detection via Region-based Fully Convolutional Networks

Jifeng Dai, Yi Li, Kaiming He, Jian Sun

R-FCN: Object Detection via Region-based Fully Convolutional Networks

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Fast recovery from a union of subspaces

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Fast recovery from a union of subspaces

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Verification Based Solution for Structured MAB Problems

Zohar S. Karnin

Verification Based Solution for Structured MAB Problems

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

Shashank Singh, Barnabás Póczos

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

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Learned Region Sparsity and Diversity Also Predicts Visual Attention

Zijun Wei, Hossein Adeli, Minh Hoai, Gregory J. Zelinsky, Dimitris Samaras

Learned Region Sparsity and Diversity Also Predicts Visual Attention

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Community Detection on Evolving Graphs

Aris Anagnostopoulos, Jakub Lacki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian

Community Detection on Evolving Graphs

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Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

Jianxu Chen, Lin Yang, Yizhe Zhang, Mark S. Alber, Danny Ziyi Chen

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

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Interpretable Distribution Features with Maximum Testing Power

Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton

Interpretable Distribution Features with Maximum Testing Power

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Observational-Interventional Priors for Dose-Response Learning

Ricardo Silva

Observational-Interventional Priors for Dose-Response Learning

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Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction

Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon

Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction

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Bayesian Intermittent Demand Forecasting for Large Inventories

Matthias W. Seeger, David Salinas, Valentin Flunkert

Bayesian Intermittent Demand Forecasting for Large Inventories

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Deep Exploration via Bootstrapped DQN

Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy

Deep Exploration via Bootstrapped DQN

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Adaptive Neural Compilation

Rudy R. Bunel, Alban Desmaison, Pawan Kumar Mudigonda, Pushmeet Kohli, Philip H. S. Torr

Adaptive Neural Compilation

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Deep Learning Games

Dale Schuurmans, Martin Zinkevich

Deep Learning Games

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Structured Matrix Recovery via the Generalized Dantzig Selector

Sheng Chen, Arindam Banerjee

Structured Matrix Recovery via the Generalized Dantzig Selector

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Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction

Jacob Steinhardt, Gregory Valiant, Moses Charikar

Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction

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FPNN: Field Probing Neural Networks for 3D Data

Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas

FPNN: Field Probing Neural Networks for 3D Data

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Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

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

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

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Iterative Refinement of the Approximate Posterior for Directed Belief Networks

Devon R. Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince D. Calhoun, Junyoung Chung

Iterative Refinement of the Approximate Posterior for Directed Belief Networks

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Mistake Bounds for Binary Matrix Completion

Mark Herbster, Stephen Pasteris, Massimiliano Pontil

Mistake Bounds for Binary Matrix Completion

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Contextual semibandits via supervised learning oracles

Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík

Contextual semibandits via supervised learning oracles

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Kernel Bayesian Inference with Posterior Regularization

Yang Song, Jun Zhu, Yong Ren

Kernel Bayesian Inference with Posterior Regularization

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Learning Sparse Gaussian Graphical Models with Overlapping Blocks

Mohammad Javad Hosseini, Su-In Lee

Learning Sparse Gaussian Graphical Models with Overlapping Blocks

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Mixed Linear Regression with Multiple Components

Kai Zhong, Prateek Jain, Inderjit S. Dhillon

Mixed Linear Regression with Multiple Components

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

Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes

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

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Variational Bayes on Monte Carlo Steroids

Aditya Grover, Stefano Ermon

Variational Bayes on Monte Carlo Steroids

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Dimension-Free Iteration Complexity of Finite Sum Optimization Problems

Yossi Arjevani, Ohad Shamir

Dimension-Free Iteration Complexity of Finite Sum Optimization Problems

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Preference Completion from Partial Rankings

Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh

Preference Completion from Partial Rankings

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Fast learning rates with heavy-tailed losses

Vu C. Dinh, Lam Si Tung Ho, Binh T. Nguyen, Duy M. H. Nguyen

Fast learning rates with heavy-tailed losses

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Generating Images with Perceptual Similarity Metrics based on Deep Networks

Alexey Dosovitskiy, Thomas Brox

Generating Images with Perceptual Similarity Metrics based on Deep Networks

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Mapping Estimation for Discrete Optimal Transport

Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard

Mapping Estimation for Discrete Optimal Transport

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Learning shape correspondence with anisotropic convolutional neural networks

Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein

Learning shape correspondence with anisotropic convolutional neural networks

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Variational Inference in Mixed Probabilistic Submodular Models

Josip Djolonga, Sebastian Tschiatschek, Andreas Krause

Variational Inference in Mixed Probabilistic Submodular Models

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Unsupervised Learning of 3D Structure from Images

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

Unsupervised Learning of 3D Structure from Images

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Examples are not enough, learn to criticize! Criticism for Interpretability

Been Kim, Oluwasanmi Koyejo, Rajiv Khanna

Examples are not enough, learn to criticize! Criticism for Interpretability

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Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease

Hao Henry Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson

Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease

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Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

Rémi Lam, Karen Willcox, David H. Wolpert

Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

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GAP Safe Screening Rules for Sparse-Group Lasso

Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

GAP Safe Screening Rules for Sparse-Group Lasso

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More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

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Exact Recovery of Hard Thresholding Pursuit

Xiao-Tong Yuan, Ping Li, Tong Zhang

Exact Recovery of Hard Thresholding Pursuit

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Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics

Wei-Shou Hsu, Pascal Poupart

Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics

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Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

Mauro Scanagatta, Giorgio Corani, Cassio Polpo de Campos, Marco Zaffalon

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

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The Forget-me-not Process

Kieran Milan, Joel Veness, James Kirkpatrick, Michael H. Bowling, Anna Koop, Demis Hassabis

The Forget-me-not Process

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Differential Privacy without Sensitivity

Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa

Differential Privacy without Sensitivity

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Structured Prediction Theory Based on Factor Graph Complexity

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

Structured Prediction Theory Based on Factor Graph Complexity

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Tagger: Deep Unsupervised Perceptual Grouping

Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Harri Valpola, Jürgen Schmidhuber

Tagger: Deep Unsupervised Perceptual Grouping

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Eliciting Categorical Data for Optimal Aggregation

Chien-Ju Ho, Rafael M. Frongillo, Yiling Chen

Eliciting Categorical Data for Optimal Aggregation

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The Product Cut

Thomas Laurent, James H. von Brecht, Xavier Bresson, Arthur Szlam

The Product Cut

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Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities

Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities

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Sparse Support Recovery with Non-smooth Loss Functions

Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques

Sparse Support Recovery with Non-smooth Loss Functions

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A Simple Practical Accelerated Method for Finite Sums

Aaron Defazio

A Simple Practical Accelerated Method for Finite Sums

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Near-Optimal Smoothing of Structured Conditional Probability Matrices

Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky

Near-Optimal Smoothing of Structured Conditional Probability Matrices

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Active Nearest-Neighbor Learning in Metric Spaces

Aryeh Kontorovich, Sivan Sabato, Ruth Urner

Active Nearest-Neighbor Learning in Metric Spaces

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Conditional Image Generation with PixelCNN Decoders

Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, Alex Graves

Conditional Image Generation with PixelCNN Decoders

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Clustering with Same-Cluster Queries

Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David

Clustering with Same-Cluster Queries

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Pruning Random Forests for Prediction on a Budget

Feng Nan, Joseph Wang, Venkatesh Saligrama

Pruning Random Forests for Prediction on a Budget

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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Chris Junchi Li, Zhaoran Wang, Han Liu

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

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Multimodal Residual Learning for Visual QA

Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, JungWoo Ha, Byoung-Tak Zhang

Multimodal Residual Learning for Visual QA

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Lifelong Learning with Weighted Majority Votes

Anastasia Pentina, Ruth Urner

Lifelong Learning with Weighted Majority Votes

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

Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter F. Stewart

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

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New Liftable Classes for First-Order Probabilistic Inference

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

New Liftable Classes for First-Order Probabilistic Inference

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Convergence guarantees for kernel-based quadrature rules in misspecified settings

Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu

Convergence guarantees for kernel-based quadrature rules in misspecified settings

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

Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang

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

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Matching Networks for One Shot Learning

Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra

Matching Networks for One Shot Learning

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

Farshad Lahouti, Babak Hassibi

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

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Guided Policy Search via Approximate Mirror Descent

William H. Montgomery, Sergey Levine

Guided Policy Search via Approximate Mirror Descent

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A posteriori error bounds for joint matrix decomposition problems

Nicolò Colombo, Nikos Vlassis

A posteriori error bounds for joint matrix decomposition problems

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Optimal Tagging with Markov Chain Optimization

Nir Rosenfeld, Amir Globerson

Optimal Tagging with Markov Chain Optimization

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Estimating the Size of a Large Network and its Communities from a Random Sample

Lin Chen, Amin Karbasi, Forrest W. Crawford

Estimating the Size of a Large Network and its Communities from a Random Sample

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A Sparse Interactive Model for Matrix Completion with Side Information

Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi

A Sparse Interactive Model for Matrix Completion with Side Information

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Assortment Optimization Under the Mallows model

Antoine Désir, Vineet Goyal, Srikanth Jagabathula, Danny Segev

Assortment Optimization Under the Mallows model

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Optimal Cluster Recovery in the Labeled Stochastic Block Model

Se-Young Yun, Alexandre Proutière

Optimal Cluster Recovery in the Labeled Stochastic Block Model

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Wasserstein Training of Restricted Boltzmann Machines

Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi

Wasserstein Training of Restricted Boltzmann Machines

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On Robustness of Kernel Clustering

Bowei Yan, Purnamrita Sarkar

On Robustness of Kernel Clustering

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Greedy Feature Construction

Dino Oglic, Thomas Gärtner

Greedy Feature Construction

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Maximization of Approximately Submodular Functions

Thibaut Horel, Yaron Singer

Maximization of Approximately Submodular Functions

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Nearly Isometric Embedding by Relaxation

James McQueen, Marina Meila, Dominique Joncas

Nearly Isometric Embedding by Relaxation

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A Non-generative Framework and Convex Relaxations for Unsupervised Learning

Elad Hazan, Tengyu Ma

A Non-generative Framework and Convex Relaxations for Unsupervised Learning

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The Limits of Learning with Missing Data

Brian Bullins, Elad Hazan, Tomer Koren

The Limits of Learning with Missing Data

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Bayesian latent structure discovery from multi-neuron recordings

Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow

Bayesian latent structure discovery from multi-neuron recordings

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Fast and Provably Good Seedings for k-Means

Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause

Fast and Provably Good Seedings for k-Means

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Bayesian Optimization for Probabilistic Programs

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

Bayesian Optimization for Probabilistic Programs

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Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering

Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah

Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering

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Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

Namrata Vaswani, Han Guo

Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated

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Coin Betting and Parameter-Free Online Learning

Francesco Orabona, Dávid Pál

Coin Betting and Parameter-Free Online Learning

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Graphons, mergeons, and so on!

Justin Eldridge, Mikhail Belkin, Yusu Wang

Graphons, mergeons, and so on!

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Generating Long-term Trajectories Using Deep Hierarchical Networks

Stephan Zheng, Yisong Yue, Jennifer Hobbs

Generating Long-term Trajectories Using Deep Hierarchical Networks

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Exponential expressivity in deep neural networks through transient chaos

Ben Poole, Subhaneil Lahiri, Maithreyi Raghu, Jascha Sohl-Dickstein, Surya Ganguli

Exponential expressivity in deep neural networks through transient chaos

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NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang

NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

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Bi-Objective Online Matching and Submodular Allocations

Hossein Esfandiari, Nitish Korula, Vahab S. Mirrokni

Bi-Objective Online Matching and Submodular Allocations

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Disease Trajectory Maps

Peter Schulam, Raman Arora

Disease Trajectory Maps

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Satisfying Real-world Goals with Dataset Constraints

Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander

Satisfying Real-world Goals with Dataset Constraints

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

Bipin Rajendran, Pulkit Tandon, Yash H. Malviya

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

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Deep Learning Models of the Retinal Response to Natural Scenes

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

Deep Learning Models of the Retinal Response to Natural Scenes

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Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David J. Crandall, Dhruv Batra

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

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Online and Differentially-Private Tensor Decomposition

Yining Wang, Anima Anandkumar

Online and Differentially-Private Tensor Decomposition

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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

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Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages

Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages

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A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order

Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu

A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order

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Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

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The Robustness of Estimator Composition

Pingfan Tang, Jeff M. Phillips

The Robustness of Estimator Composition

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Convex Two-Layer Modeling with Latent Structure

Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

Convex Two-Layer Modeling with Latent Structure

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

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

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

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Learning Kernels with Random Features

Aman Sinha, John C. Duchi

Learning Kernels with Random Features

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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

Tim Salimans, Diederik P. Kingma

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

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Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko

Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

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Value Iteration Networks

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

Value Iteration Networks

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Identification and Overidentification of Linear Structural Equation Models

Bryant Chen

Identification and Overidentification of Linear Structural Equation Models

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Proximal Deep Structured Models

Shenlong Wang, Sanja Fidler, Raquel Urtasun

Proximal Deep Structured Models

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Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics

Travis Monk, Cristina Savin, Jörg Lücke

Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics

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Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning

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

Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning

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Probabilistic Inference with Generating Functions for Poisson Latent Variable Models

Kevin Winner, Daniel R. Sheldon

Probabilistic Inference with Generating Functions for Poisson Latent Variable Models

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Learning under uncertainty: a comparison between R-W and Bayesian approach

He Huang, Martin P. Paulus

Learning under uncertainty: a comparison between R-W and Bayesian approach

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Regularized Nonlinear Acceleration

Damien Scieur, Alexandre d'Aspremont, Francis R. Bach

Regularized Nonlinear Acceleration

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Learning the Number of Neurons in Deep Networks

Jose M. Alvarez, Mathieu Salzmann

Learning the Number of Neurons in Deep Networks

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Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random

Ilya Shpitser

Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random

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Unsupervised Learning for Physical Interaction through Video Prediction

Chelsea Finn, Ian J. Goodfellow, Sergey Levine

Unsupervised Learning for Physical Interaction through Video Prediction

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Scalable Adaptive Stochastic Optimization Using Random Projections

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

Scalable Adaptive Stochastic Optimization Using Random Projections

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

Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen

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

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Integrated perception with recurrent multi-task neural networks

Hakan Bilen, Andrea Vedaldi

Integrated perception with recurrent multi-task neural networks

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Learning Structured Sparsity in Deep Neural Networks

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

Learning Structured Sparsity in Deep Neural Networks

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A Probabilistic Model of Social Decision Making based on Reward Maximization

Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao

A Probabilistic Model of Social Decision Making based on Reward Maximization

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Natural-Parameter Networks: A Class of Probabilistic Neural Networks

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Natural-Parameter Networks: A Class of Probabilistic Neural Networks

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Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates

Yuanzhi Li, Yingyu Liang, Andrej Risteski

Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates

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Reconstructing Parameters of Spreading Models from Partial Observations

Andrey Y. Lokhov

Reconstructing Parameters of Spreading Models from Partial Observations

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Batched Gaussian Process Bandit Optimization via Determinantal Point Processes

Tarun Kathuria, Amit Deshpande, Pushmeet Kohli

Batched Gaussian Process Bandit Optimization via Determinantal Point Processes

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Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games

Sougata Chaudhuri, Ambuj Tewari

Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games

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

Christopher Lynn, Daniel D. Lee

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

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Rényi Divergence Variational Inference

Yingzhen Li, Richard E. Turner

Rényi Divergence Variational Inference

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Robustness of classifiers: from adversarial to random noise

Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Robustness of classifiers: from adversarial to random noise

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Learning Sensor Multiplexing Design through Back-propagation

Ayan Chakrabarti

Learning Sensor Multiplexing Design through Back-propagation

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A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control

Ivan Herreros, Xerxes D. Arsiwalla, Paul F. M. J. Verschure

A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control

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On Explore-Then-Commit strategies

Aurélien Garivier, Tor Lattimore, Emilie Kaufmann

On Explore-Then-Commit strategies

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Fast Active Set Methods for Online Spike Inference from Calcium Imaging

Johannes Friedrich, Liam Paninski

Fast Active Set Methods for Online Spike Inference from Calcium Imaging

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Bayesian optimization for automated model selection

Gustavo Malkomes, Chip Schaff, Roman Garnett

Bayesian optimization for automated model selection

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Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices

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

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices

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Parameter Learning for Log-supermodular Distributions

Tatiana Shpakova, Francis R. Bach

Parameter Learning for Log-supermodular Distributions

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Generative Adversarial Imitation Learning

Jonathan Ho, Stefano Ermon

Generative Adversarial Imitation Learning

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Inference by Reparameterization in Neural Population Codes

Rajkumar Vasudeva Raju, Xaq Pitkow

Inference by Reparameterization in Neural Population Codes

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

Pan Zhang

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

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Improving PAC Exploration Using the Median Of Means

Jason Pazis, Ronald Parr, Jonathan P. How

Improving PAC Exploration Using the Median Of Means

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A Pseudo-Bayesian Algorithm for Robust PCA

Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf

A Pseudo-Bayesian Algorithm for Robust PCA

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Estimating the class prior and posterior from noisy positives and unlabeled data

Shantanu Jain, Martha White, Predrag Radivojac

Estimating the class prior and posterior from noisy positives and unlabeled data

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Ancestral Causal Inference

Sara Magliacane, Tom Claassen, Joris M. Mooij

Ancestral Causal Inference

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DeepMath - Deep Sequence Models for Premise Selection

Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Eén, François Chollet, Josef Urban

DeepMath - Deep Sequence Models for Premise Selection

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Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making

Himabindu Lakkaraju, Jure Leskovec

Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making

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Simple and Efficient Weighted Minwise Hashing

Anshumali Shrivastava

Simple and Efficient Weighted Minwise Hashing

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CRF-CNN: Modeling Structured Information in Human Pose Estimation

Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang

CRF-CNN: Modeling Structured Information in Human Pose Estimation

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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

Lalit Jain, Kevin G. Jamieson, Robert D. Nowak

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

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Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions

Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal

Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions

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CliqueCNN: Deep Unsupervised Exemplar Learning

Miguel Ángel Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Björn Ommer

CliqueCNN: Deep Unsupervised Exemplar Learning

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Linear Relaxations for Finding Diverse Elements in Metric Spaces

Aditya Bhaskara, Mehrdad Ghadiri, Vahab S. Mirrokni, Ola Svensson

Linear Relaxations for Finding Diverse Elements in Metric Spaces

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SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling

Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros

SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling

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Strategic Attentive Writer for Learning Macro-Actions

Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, Koray Kavukcuoglu

Strategic Attentive Writer for Learning Macro-Actions

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Average-case hardness of RIP certification

Tengyao Wang, Quentin Berthet, Yaniv Plan

Average-case hardness of RIP certification

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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

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Regret Bounds for Non-decomposable Metrics with Missing Labels

Nagarajan Natarajan, Prateek Jain

Regret Bounds for Non-decomposable Metrics with Missing Labels

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Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares

Rong Zhu

Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares

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Deep Submodular Functions: Definitions and Learning

Brian W. Dolhansky, Jeff A. Bilmes

Deep Submodular Functions: Definitions and Learning

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Threshold Bandits, With and Without Censored Feedback

Jacob D. Abernethy, Kareem Amin, Ruihao Zhu

Threshold Bandits, With and Without Censored Feedback

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Asynchronous Parallel Greedy Coordinate Descent

Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh

Asynchronous Parallel Greedy Coordinate Descent

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Dual Learning for Machine Translation

Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma

Dual Learning for Machine Translation

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An ensemble diversity approach to supervised binary hashing

Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei

An ensemble diversity approach to supervised binary hashing

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Exponential Family Embeddings

Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

Exponential Family Embeddings

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Cooperative Inverse Reinforcement Learning

Dylan Hadfield-Menell, Stuart J. Russell, Pieter Abbeel, Anca D. Dragan

Cooperative Inverse Reinforcement Learning

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Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale

Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale

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Refined Lower Bounds for Adversarial Bandits

Sébastien Gerchinovitz, Tor Lattimore

Refined Lower Bounds for Adversarial Bandits

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Large Margin Discriminant Dimensionality Reduction in Prediction Space

Mohammad J. Saberian, Jose Costa Pereira, Nuno Vasconcelos, Can Xu

Large Margin Discriminant Dimensionality Reduction in Prediction Space

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The Power of Adaptivity in Identifying Statistical Alternatives

Kevin G. Jamieson, Daniel Haas, Benjamin Recht

The Power of Adaptivity in Identifying Statistical Alternatives

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Unsupervised Risk Estimation Using Only Conditional Independence Structure

Jacob Steinhardt, Percy S. Liang

Unsupervised Risk Estimation Using Only Conditional Independence Structure

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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Sebastian Nowozin, Botond Cseke, Ryota Tomioka

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

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Sample Complexity of Automated Mechanism Design

Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik

Sample Complexity of Automated Mechanism Design

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Communication-Optimal Distributed Clustering

Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang

Communication-Optimal Distributed Clustering

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CMA-ES with Optimal Covariance Update and Storage Complexity

Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel

CMA-ES with Optimal Covariance Update and Storage Complexity

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Local Similarity-Aware Deep Feature Embedding

Chen Huang, Chen Change Loy, Xiaoou Tang

Local Similarity-Aware Deep Feature Embedding

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Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

Hao Wang, Xingjian Shi, Dit-Yan Yeung

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

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The Multi-fidelity Multi-armed Bandit

Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider

The Multi-fidelity Multi-armed Bandit

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Supervised Learning with Tensor Networks

Edwin Miles Stoudenmire, David J. Schwab

Supervised Learning with Tensor Networks

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Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision

Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee

Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision

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Measuring Neural Net Robustness with Constraints

Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya V. Nori, Antonio Criminisi

Measuring Neural Net Robustness with Constraints

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Global Analysis of Expectation Maximization for Mixtures of Two Gaussians

Ji Xu, Daniel J. Hsu, Arian Maleki

Global Analysis of Expectation Maximization for Mixtures of Two Gaussians

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Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models

Juho Lee, Lancelot F. James, Seungjin Choi

Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models

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Search Improves Label for Active Learning

Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang

Search Improves Label for Active Learning

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Professor Forcing: A New Algorithm for Training Recurrent Networks

Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio

Professor Forcing: A New Algorithm for Training Recurrent Networks

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High Dimensional Structured Superposition Models

Qilong Gu, Arindam Banerjee

High Dimensional Structured Superposition Models

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The Multiple Quantile Graphical Model

Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani

The Multiple Quantile Graphical Model

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Learning values across many orders of magnitude

Hado P. van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

Learning values across many orders of magnitude

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Multi-armed Bandits: Competing with Optimal Sequences

Zohar S. Karnin, Oren Anava

Multi-armed Bandits: Competing with Optimal Sequences

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Gaussian Processes for Survival Analysis

Tamara Fernandez, Nicolas Rivera, Yee Whye Teh

Gaussian Processes for Survival Analysis

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Reshaped Wirtinger Flow for Solving Quadratic System of Equations

Huishuai Zhang, Yingbin Liang

Reshaped Wirtinger Flow for Solving Quadratic System of Equations

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Spectral Learning of Dynamic Systems from Nonequilibrium Data

Hao Wu, Frank Noé

Spectral Learning of Dynamic Systems from Nonequilibrium Data

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Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

George Papamakarios, Iain Murray

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

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Using Fast Weights to Attend to the Recent Past

Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu

Using Fast Weights to Attend to the Recent Past

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Barzilai-Borwein Step Size for Stochastic Gradient Descent

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

Barzilai-Borwein Step Size for Stochastic Gradient Descent

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Improved Dropout for Shallow and Deep Learning

Zhe Li, Boqing Gong, Tianbao Yang

Improved Dropout for Shallow and Deep Learning

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Stochastic Variational Deep Kernel Learning

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

Stochastic Variational Deep Kernel Learning

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Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo

Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard

Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo

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A Bio-inspired Redundant Sensing Architecture

Anh Tuan Nguyen, Jian Xu, Zhi Yang

A Bio-inspired Redundant Sensing Architecture

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

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

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

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

Théodore Bluche

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

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

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

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

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Deep Learning for Predicting Human Strategic Behavior

Jason S. Hartford, James R. Wright, Kevin Leyton-Brown

Deep Learning for Predicting Human Strategic Behavior

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SURGE: Surface Regularized Geometry Estimation from a Single Image

Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian L. Price, Alan L. Yuille

SURGE: Surface Regularized Geometry Estimation from a Single Image

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Robust k-means: a Theoretical Revisit

Alexandros Georgogiannis

Robust k-means: a Theoretical Revisit

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Stochastic Gradient Geodesic MCMC Methods

Chang Liu, Jun Zhu, Yang Song

Stochastic Gradient Geodesic MCMC Methods

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Doubly Convolutional Neural Networks

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

Doubly Convolutional Neural Networks

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Generating Videos with Scene Dynamics

Carl Vondrick, Hamed Pirsiavash, Antonio Torralba

Generating Videos with Scene Dynamics

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Minimizing Quadratic Functions in Constant Time

Kohei Hayashi, Yuichi Yoshida

Minimizing Quadratic Functions in Constant Time

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Unifying Count-Based Exploration and Intrinsic Motivation

Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos

Unifying Count-Based Exploration and Intrinsic Motivation

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Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain

Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon

Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain

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Causal Bandits: Learning Good Interventions via Causal Inference

Finnian Lattimore, Tor Lattimore, Mark D. Reid

Causal Bandits: Learning Good Interventions via Causal Inference

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Learning and Forecasting Opinion Dynamics in Social Networks

Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez-Rodriguez

Learning and Forecasting Opinion Dynamics in Social Networks

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Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

Chengtao Li, Suvrit Sra, Stefanie Jegelka

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

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

Grégory Rogez, Cordelia Schmid

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

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Latent Attention For If-Then Program Synthesis

Chang Liu, Xinyun Chen, Eui Chul Richard Shin, Mingcheng Chen, Dawn Xiaodong Song

Latent Attention For If-Then Program Synthesis

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Learning Parametric Sparse Models for Image Super-Resolution

Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu

Learning Parametric Sparse Models for Image Super-Resolution

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Adaptive optimal training of animal behavior

Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow

Adaptive optimal training of animal behavior

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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Yarin Gal, Zoubin Ghahramani

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

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Leveraging Sparsity for Efficient Submodular Data Summarization

Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis

Leveraging Sparsity for Efficient Submodular Data Summarization

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High-Rank Matrix Completion and Clustering under Self-Expressive Models

Ehsan Elhamifar

High-Rank Matrix Completion and Clustering under Self-Expressive Models

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General Tensor Spectral Co-clustering for Higher-Order Data

Tao Wu, Austin R. Benson, David F. Gleich

General Tensor Spectral Co-clustering for Higher-Order Data

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An algorithm for L1 nearest neighbor search via monotonic embedding

Xinan Wang, Sanjoy Dasgupta

An algorithm for L1 nearest neighbor search via monotonic embedding

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Tracking the Best Expert in Non-stationary Stochastic Environments

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

Tracking the Best Expert in Non-stationary Stochastic Environments

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Achieving budget-optimality with adaptive schemes in crowdsourcing

Ashish Khetan, Sewoong Oh

Achieving budget-optimality with adaptive schemes in crowdsourcing

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How Deep is the Feature Analysis underlying Rapid Visual Categorization?

Sven Eberhardt, Jonah G. Cader, Thomas Serre

How Deep is the Feature Analysis underlying Rapid Visual Categorization?

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Stochastic Three-Composite Convex Minimization

Alp Yurtsever, Bang Công Vu, Volkan Cevher

Stochastic Three-Composite Convex Minimization

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Optimal spectral transportation with application to music transcription

Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya

Optimal spectral transportation with application to music transcription

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Safe Policy Improvement by Minimizing Robust Baseline Regret

Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow

Safe Policy Improvement by Minimizing Robust Baseline Regret

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

Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre M. Bayen

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

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Scaled Least Squares Estimator for GLMs in Large-Scale Problems

Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati

Scaled Least Squares Estimator for GLMs in Large-Scale Problems

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Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation

Emmanuel Abbe, Colin Sandon

Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation

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What Makes Objects Similar: A Unified Multi-Metric Learning Approach

Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou

What Makes Objects Similar: A Unified Multi-Metric Learning Approach

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Selective inference for group-sparse linear models

Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty

Selective inference for group-sparse linear models

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Linear dynamical neural population models through nonlinear embeddings

Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham

Linear dynamical neural population models through nonlinear embeddings

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Fast and accurate spike sorting of high-channel count probes with KiloSort

Marius Pachitariu, Nicholas A. Steinmetz, Shabnam N. Kadir, Matteo Carandini, Kenneth D. Harris

Fast and accurate spike sorting of high-channel count probes with KiloSort

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Improved Deep Metric Learning with Multi-class N-pair Loss Objective

Kihyuk Sohn

Improved Deep Metric Learning with Multi-class N-pair Loss Objective

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Faster Projection-free Convex Optimization over the Spectrahedron

Dan Garber

Faster Projection-free Convex Optimization over the Spectrahedron

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Sequential Neural Models with Stochastic Layers

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

Sequential Neural Models with Stochastic Layers

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Optimal Learning for Multi-pass Stochastic Gradient Methods

Junhong Lin, Lorenzo Rosasco

Optimal Learning for Multi-pass Stochastic Gradient Methods

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Memory-Efficient Backpropagation Through Time

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

Memory-Efficient Backpropagation Through Time

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Higher-Order Factorization Machines

Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata

Higher-Order Factorization Machines

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A Unified Approach for Learning the Parameters of Sum-Product Networks

Han Zhao, Pascal Poupart, Geoffrey J. Gordon

A Unified Approach for Learning the Parameters of Sum-Product Networks

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Structure-Blind Signal Recovery

Dmitry Ostrovsky, Zaïd Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

Structure-Blind Signal Recovery

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Structured Sparse Regression via Greedy Hard Thresholding

Prateek Jain, Nikhil Rao, Inderjit S. Dhillon

Structured Sparse Regression via Greedy Hard Thresholding

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Conditional Generative Moment-Matching Networks

Yong Ren, Jun Zhu, Jialian Li, Yucen Luo

Conditional Generative Moment-Matching Networks

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Causal meets Submodular: Subset Selection with Directed Information

Yuxun Zhou, Costas J. Spanos

Causal meets Submodular: Subset Selection with Directed Information

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

Dennis Wei

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

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Distributed Flexible Nonlinear Tensor Factorization

Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani

Distributed Flexible Nonlinear Tensor Factorization

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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, Jens Meiler, Daniel Cremers

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

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Towards Conceptual Compression

Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

Towards Conceptual Compression

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Equality of Opportunity in Supervised Learning

Moritz Hardt, Eric Price, Nati Srebro

Equality of Opportunity in Supervised Learning

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Measuring the reliability of MCMC inference with bidirectional Monte Carlo

Roger B. Grosse, Siddharth Ancha, Daniel M. Roy

Measuring the reliability of MCMC inference with bidirectional Monte Carlo

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

Chi Jin, Sham M. Kakade, Praneeth Netrapalli

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

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An Architecture for Deep, Hierarchical Generative Models

Philip Bachman

An Architecture for Deep, Hierarchical Generative Models

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CNNpack: Packing Convolutional Neural Networks in the Frequency Domain

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

CNNpack: Packing Convolutional Neural Networks in the Frequency Domain

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Domain Separation Networks

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

Domain Separation Networks

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Algorithms and matching lower bounds for approximately-convex optimization

Andrej Risteski, Yuanzhi Li

Algorithms and matching lower bounds for approximately-convex optimization

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Statistical Inference for Cluster Trees

Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman

Statistical Inference for Cluster Trees

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Incremental Variational Sparse Gaussian Process Regression

Ching-An Cheng, Byron Boots

Incremental Variational Sparse Gaussian Process Regression

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Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

Weihao Gao, Sewoong Oh, Pramod Viswanath

Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

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Adaptive Averaging in Accelerated Descent Dynamics

Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett

Adaptive Averaging in Accelerated Descent Dynamics

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Stochastic Gradient MCMC with Stale Gradients

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

Stochastic Gradient MCMC with Stale Gradients

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Nested Mini-Batch K-Means

James Newling, François Fleuret

Nested Mini-Batch K-Means

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Optimal Architectures in a Solvable Model of Deep Networks

Jonathan Kadmon, Haim Sompolinsky

Optimal Architectures in a Solvable Model of Deep Networks

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Learning Deep Embeddings with Histogram Loss

Evgeniya Ustinova, Victor S. Lempitsky

Learning Deep Embeddings with Histogram Loss

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Kronecker Determinantal Point Processes

Zelda E. Mariet, Suvrit Sra

Kronecker Determinantal Point Processes

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Fast Distributed Submodular Cover: Public-Private Data Summarization

Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi

Fast Distributed Submodular Cover: Public-Private Data Summarization

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Linear Feature Encoding for Reinforcement Learning

Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin

Linear Feature Encoding for Reinforcement Learning

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Deep Neural Networks with Inexact Matching for Person Re-Identification

Arulkumar Subramaniam, Moitreya Chatterjee, Anurag Mittal

Deep Neural Networks with Inexact Matching for Person Re-Identification

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Semiparametric Differential Graph Models

Pan Xu, Quanquan Gu

Semiparametric Differential Graph Models

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Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

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

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

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Hardness of Online Sleeping Combinatorial Optimization Problems

Satyen Kale, Chansoo Lee, Dávid Pál

Hardness of Online Sleeping Combinatorial Optimization Problems

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Automated scalable segmentation of neurons from multispectral images

Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski

Automated scalable segmentation of neurons from multispectral images

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Object based Scene Representations using Fisher Scores of Local Subspace Projections

Mandar Dixit, Nuno Vasconcelos

Object based Scene Representations using Fisher Scores of Local Subspace Projections

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Bayesian Optimization with Robust Bayesian Neural Networks

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

Bayesian Optimization with Robust Bayesian Neural Networks

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Tight Complexity Bounds for Optimizing Composite Objectives

Blake E. Woodworth, Nati Srebro

Tight Complexity Bounds for Optimizing Composite Objectives

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Solving Marginal MAP Problems with NP Oracles and Parity Constraints

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

Solving Marginal MAP Problems with NP Oracles and Parity Constraints

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Sub-sampled Newton Methods with Non-uniform Sampling

Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney

Sub-sampled Newton Methods with Non-uniform Sampling

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Ladder Variational Autoencoders

Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther

Ladder Variational Autoencoders

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Geometric Dirichlet Means Algorithm for topic inference

Mikhail Yurochkin, XuanLong Nguyen

Geometric Dirichlet Means Algorithm for topic inference

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Optimal Binary Classifier Aggregation for General Losses

Akshay Balsubramani, Yoav Freund

Optimal Binary Classifier Aggregation for General Losses

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Low-Rank Regression with Tensor Responses

Guillaume Rabusseau, Hachem Kadri

Low-Rank Regression with Tensor Responses

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Deconvolving Feedback Loops in Recommender Systems

Ayan Sinha, David F. Gleich, Karthik Ramani

Deconvolving Feedback Loops in Recommender Systems

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A Powerful Generative Model Using Random Weights for the Deep Image Representation

Kun He, Yan Wang, John E. Hopcroft

A Powerful Generative Model Using Random Weights for the Deep Image Representation

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SDP Relaxation with Randomized Rounding for Energy Disaggregation

Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu

SDP Relaxation with Randomized Rounding for Energy Disaggregation

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Joint quantile regression in vector-valued RKHSs

Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc

Joint quantile regression in vector-valued RKHSs

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Understanding Probabilistic Sparse Gaussian Process Approximations

Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

Understanding Probabilistic Sparse Gaussian Process Approximations

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Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes

Hassan A. Kingravi, Harshal R. Maske, Girish Chowdhary

Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes

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The Generalized Reparameterization Gradient

Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

The Generalized Reparameterization Gradient

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Fast Algorithms for Robust PCA via Gradient Descent

Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis

Fast Algorithms for Robust PCA via Gradient Descent

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On the Recursive Teaching Dimension of VC Classes

Xi Chen, Yu Cheng, Bo Tang

On the Recursive Teaching Dimension of VC Classes

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Can Active Memory Replace Attention?

Lukasz Kaiser, Samy Bengio

Can Active Memory Replace Attention?

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Stochastic Variance Reduction Methods for Saddle-Point Problems

Balamurugan Palaniappan, Francis R. Bach

Stochastic Variance Reduction Methods for Saddle-Point Problems

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Computational and Statistical Tradeoffs in Learning to Rank

Ashish Khetan, Sewoong Oh

Computational and Statistical Tradeoffs in Learning to Rank

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On Multiplicative Integration with Recurrent Neural Networks

Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov

On Multiplicative Integration with Recurrent Neural Networks

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Clustering with Bregman Divergences: an Asymptotic Analysis

Chaoyue Liu, Mikhail Belkin

Clustering with Bregman Divergences: an Asymptotic Analysis

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Learning Bound for Parameter Transfer Learning

Wataru Kumagai

Learning Bound for Parameter Transfer Learning

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SoundNet: Learning Sound Representations from Unlabeled Video

Yusuf Aytar, Carl Vondrick, Antonio Torralba

SoundNet: Learning Sound Representations from Unlabeled Video

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Discriminative Gaifman Models

Mathias Niepert

Discriminative Gaifman Models

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

Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman

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

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Improving Variational Autoencoders with Inverse Autoregressive Flow

Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling

Improving Variational Autoencoders with Inverse Autoregressive Flow

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Multi-step learning and underlying structure in statistical models

Maia Fraser

Multi-step learning and underlying structure in statistical models

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Learnable Visual Markers

Oleg Grinchuk, Vadim Lebedev, Victor S. Lempitsky

Learnable Visual Markers

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Large-Scale Price Optimization via Network Flow

Shinji Ito, Ryohei Fujimaki

Large-Scale Price Optimization via Network Flow

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One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities

Michalis K. Titsias

One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities

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Pairwise Choice Markov Chains

Stephen Ragain, Johan Ugander

Pairwise Choice Markov Chains

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Agnostic Estimation for Misspecified Phase Retrieval Models

Matey Neykov, Zhaoran Wang, Han Liu

Agnostic Estimation for Misspecified Phase Retrieval Models

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

Xinghua Lou, Ken Kansky, Wolfgang Lehrach, C. C. Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George

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

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A Probabilistic Framework for Deep Learning

Ankit B. Patel, Minh Tan Nguyen, Richard G. Baraniuk

A Probabilistic Framework for Deep Learning

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Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back

Vitaly Feldman

Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back

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Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions

Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich

Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions

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Blind Attacks on Machine Learners

Alex Beatson, Zhaoran Wang, Han Liu

Blind Attacks on Machine Learners

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Learning Transferrable Representations for Unsupervised Domain Adaptation

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

Learning Transferrable Representations for Unsupervised Domain Adaptation

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SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques

Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky

SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques

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PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

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Hierarchical Clustering via Spreading Metrics

Aurko Roy, Sebastian Pokutta

Hierarchical Clustering via Spreading Metrics

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Fast and Flexible Monotonic Functions with Ensembles of Lattices

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

Fast and Flexible Monotonic Functions with Ensembles of Lattices

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Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization

Tyler B. Johnson, Carlos Guestrin

Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization

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PAC Reinforcement Learning with Rich Observations

Akshay Krishnamurthy, Alekh Agarwal, John Langford

PAC Reinforcement Learning with Rich Observations

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A Minimax Approach to Supervised Learning

Farzan Farnia, David Tse

A Minimax Approach to Supervised Learning

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Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow

Gang Wang, Georgios B. Giannakis

Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow

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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

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Convolutional Neural Fabrics

Shreyas Saxena, Jakob Verbeek

Convolutional Neural Fabrics

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On Regularizing Rademacher Observation Losses

Richard Nock

On Regularizing Rademacher Observation Losses

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Supervised learning through the lens of compression

Ofir David, Shay Moran, Amir Yehudayoff

Supervised learning through the lens of compression

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Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation

Xiao-Tong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu

Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation

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Matrix Completion has No Spurious Local Minimum

Rong Ge, Jason D. Lee, Tengyu Ma

Matrix Completion has No Spurious Local Minimum

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Bootstrap Model Aggregation for Distributed Statistical Learning

Jun Han, Qiang Liu

Bootstrap Model Aggregation for Distributed Statistical Learning

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Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

Zhen Xu, Wen Dong, Sargur N. Srihari

Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

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

Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher

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

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Boosting with Abstention

Corinna Cortes, Giulia DeSalvo, Mehryar Mohri

Boosting with Abstention

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Brains on Beats

Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven, Marcel A. J. van Gerven

Brains on Beats

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Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information

Alexander Shishkin, Anastasia A. Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov

Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information

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Computing and maximizing influence in linear threshold and triggering models

Justin T. Khim, Varun Jog, Po-Ling Loh

Computing and maximizing influence in linear threshold and triggering models

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Sorting out typicality with the inverse moment matrix SOS polynomial

Edouard Pauwels, Jean B. Lasserre

Sorting out typicality with the inverse moment matrix SOS polynomial

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An Online Sequence-to-Sequence Model Using Partial Conditioning

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

An Online Sequence-to-Sequence Model Using Partial Conditioning

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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

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

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Adaptive Concentration Inequalities for Sequential Decision Problems

Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon

Adaptive Concentration Inequalities for Sequential Decision Problems

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Combinatorial Multi-Armed Bandit with General Reward Functions

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

Combinatorial Multi-Armed Bandit with General Reward Functions

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Learning User Perceived Clusters with Feature-Level Supervision

Ting-Yu Cheng, Guiguan Lin, Xinyang Gong, Kang-Jun Liu, Shan-Hung Wu

Learning User Perceived Clusters with Feature-Level Supervision

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Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition

Ahmed Ibrahim, Mihaela van der Schaar

Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition

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Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods

Cristina Savin, Gasper Tkacik

Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods

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Dynamic Network Surgery for Efficient DNNs

Yiwen Guo, Anbang Yao, Yurong Chen

Dynamic Network Surgery for Efficient DNNs

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Supervised Word Mover's Distance

Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger

Supervised Word Mover's Distance

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Variational Autoencoder for Deep Learning of Images, Labels and Captions

Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin

Variational Autoencoder for Deep Learning of Images, Labels and Captions

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Learning Influence Functions from Incomplete Observations

Xinran He, Ke Xu, David Kempe, Yan Liu

Learning Influence Functions from Incomplete Observations

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Poisson-Gamma dynamical systems

Aaron Schein, Hanna M. Wallach, Mingyuan Zhou

Poisson-Gamma dynamical systems

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Combinatorial Energy Learning for Image Segmentation

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

Combinatorial Energy Learning for Image Segmentation

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Combining Low-Density Separators with CNNs

Yu-Xiong Wang, Martial Hebert

Combining Low-Density Separators with CNNs

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Binarized Neural Networks

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

Binarized Neural Networks

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Coresets for Scalable Bayesian Logistic Regression

Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

Coresets for Scalable Bayesian Logistic Regression

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Dimensionality Reduction of Massive Sparse Datasets Using Coresets

Dan Feldman, Mikhail Volkov, Daniela Rus

Dimensionality Reduction of Massive Sparse Datasets Using Coresets

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A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics

William Hoiles, Mihaela van der Schaar

A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics

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Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition

Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao

Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition

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Single Pass PCA of Matrix Products

Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis

Single Pass PCA of Matrix Products

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A Consistent Regularization Approach for Structured Prediction

Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi

A Consistent Regularization Approach for Structured Prediction

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Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments

Ransalu Senanayake, Lionel Ott, Simon Timothy O'Callaghan, Fabio Tozeto Ramos

Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments

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Operator Variational Inference

Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei

Operator Variational Inference

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A Multi-Batch L-BFGS Method for Machine Learning

Albert S. Berahas, Jorge Nocedal, Martin Takác

A Multi-Batch L-BFGS Method for Machine Learning

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Density Estimation via Discrepancy Based Adaptive Sequential Partition

Dangna Li, Kun Yang, Wing Hung Wong

Density Estimation via Discrepancy Based Adaptive Sequential Partition

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Graphical Time Warping for Joint Alignment of Multiple Curves

Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu

Graphical Time Warping for Joint Alignment of Multiple Curves

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Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Jack W. Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

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Combinatorial semi-bandit with known covariance

Rémy Degenne, Vianney Perchet

Combinatorial semi-bandit with known covariance

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Safe and Efficient Off-Policy Reinforcement Learning

Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare

Safe and Efficient Off-Policy Reinforcement Learning

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Split LBI: An Iterative Regularization Path with Structural Sparsity

Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao

Split LBI: An Iterative Regularization Path with Structural Sparsity

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Improved Techniques for Training GANs

Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen

Improved Techniques for Training GANs

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Global Optimality of Local Search for Low Rank Matrix Recovery

Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Global Optimality of Local Search for Low Rank Matrix Recovery

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A primal-dual method for conic constrained distributed optimization problems

Necdet Serhat Aybat, Erfan Yazdandoost Hamedani

A primal-dual method for conic constrained distributed optimization problems

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Local Minimax Complexity of Stochastic Convex Optimization

Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu

Local Minimax Complexity of Stochastic Convex Optimization

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Tree-Structured Reinforcement Learning for Sequential Object Localization

Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan

Tree-Structured Reinforcement Learning for Sequential Object Localization

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Learning brain regions via large-scale online structured sparse dictionary learning

Elvis Dohmatob, Arthur Mensch, Gaël Varoquaux, Bertrand Thirion

Learning brain regions via large-scale online structured sparse dictionary learning

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Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds

Hongyi Zhang, Sashank J. Reddi, Suvrit Sra

Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds

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Dynamic Filter Networks

Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc Van Gool

Dynamic Filter Networks

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Infinite Hidden Semi-Markov Modulated Interaction Point Process

Peng Lin, Bang Zhang, Ting Guo, Yang Wang, Fang Chen

Infinite Hidden Semi-Markov Modulated Interaction Point Process

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Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling

Maria-Florina Balcan, Hongyang Zhang

Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling

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Quantum Perceptron Models

Ashish Kapoor, Nathan Wiebe, Krysta Marie Svore

Quantum Perceptron Models

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Mixed vine copulas as joint models of spike counts and local field potentials

Arno Onken, Stefano Panzeri

Mixed vine copulas as joint models of spike counts and local field potentials

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Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization

Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy

Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization

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Unsupervised Learning from Noisy Networks with Applications to Hi-C Data

Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje

Unsupervised Learning from Noisy Networks with Applications to Hi-C Data

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

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

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

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Diffusion-Convolutional Neural Networks

James Atwood, Don Towsley

Diffusion-Convolutional Neural Networks

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A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification

Steven Cheng-Xian Li, Benjamin M. Marlin

A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification

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Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua

Learning a Metric Embedding for Face Recognition using the Multibatch Method

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Learning in Games: Robustness of Fast Convergence

Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos

Learning in Games: Robustness of Fast Convergence

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Quantized Random Projections and Non-Linear Estimation of Cosine Similarity

Ping Li, Michael Mitzenmacher, Martin Slawski

Quantized Random Projections and Non-Linear Estimation of Cosine Similarity

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Maximal Sparsity with Deep Networks?

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang

Maximal Sparsity with Deep Networks?

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Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits

Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits

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Direct Feedback Alignment Provides Learning in Deep Neural Networks

Arild Nøkland

Direct Feedback Alignment Provides Learning in Deep Neural Networks

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Adaptive Smoothed Online Multi-Task Learning

Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang

Adaptive Smoothed Online Multi-Task Learning

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Regret of Queueing Bandits

Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai

Regret of Queueing Bandits

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Beyond Exchangeability: The Chinese Voting Process

Moontae Lee, Seok Hyun Jin, David M. Mimno

Beyond Exchangeability: The Chinese Voting Process

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Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

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Learning feed-forward one-shot learners

Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi

Learning feed-forward one-shot learners

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

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

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

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Online Pricing with Strategic and Patient Buyers

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

Online Pricing with Strategic and Patient Buyers

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Completely random measures for modelling block-structured sparse networks

Tue Herlau, Mikkel N. Schmidt, Morten Mørup

Completely random measures for modelling block-structured sparse networks

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Efficient Neural Codes under Metabolic Constraints

Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee

Efficient Neural Codes under Metabolic Constraints

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

Damek Davis, Brent Edmunds, Madeleine Udell

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

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Double Thompson Sampling for Dueling Bandits

Huasen Wu, Xin Liu

Double Thompson Sampling for Dueling Bandits

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Accelerating Stochastic Composition Optimization

Mengdi Wang, Ji Liu, Ethan X. Fang

Accelerating Stochastic Composition Optimization

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MetaGrad: Multiple Learning Rates in Online Learning

Tim van Erven, Wouter M. Koolen

MetaGrad: Multiple Learning Rates in Online Learning

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Spatiotemporal Residual Networks for Video Action Recognition

Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes

Spatiotemporal Residual Networks for Video Action Recognition

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Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models

Tomoharu Iwata, Makoto Yamada

Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models

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Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?

Arturo Deza, Miguel P. Eckstein

Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?

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Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

Daniel Neil, Michael Pfeiffer, Shih-Chii Liu

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

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Normalized Spectral Map Synchronization

Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi

Normalized Spectral Map Synchronization

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