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JMLR Workshop and Conference Proceedings

Volume 37: Proceedings of The 32nd International Conference on Machine Learning

Editors: Francis Bach, David Blei


Accepted Papers

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Peilin Zhao, Tong Zhang

Approval Voting and Incentives in Crowdsourcing

Nihar Shah, Dengyong Zhou, Yuval Peres

A low variance consistent test of relative dependency

Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko

An Aligned Subtree Kernel for Weighted Graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock

Spectral Clustering via the Power Method - Provably

Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens

Information Geometry and Minimum Description Length Networks

Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation

Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele

Adaptive Stochastic Alternating Direction Method of Multipliers

Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li

A Lower Bound for the Optimization of Finite Sums

Alekh Agarwal, Leon Bottou

Learning Word Representations with Hierarchical Sparse Coding

Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith

Learning Transferable Features with Deep Adaptation Networks

Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan

Robust partially observable Markov decision process

Takayuki Osogami

On the Relationship between Sum-Product Networks and Bayesian Networks

Han Zhao, Mazen Melibari, Pascal Poupart

Learning from Corrupted Binary Labels via Class-Probability Estimation

Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate

Ohad Shamir

Attribute Efficient Linear Regression with Distribution-Dependent Sampling

Doron Kukliansky, Ohad Shamir

Learning Local Invariant Mahalanobis Distances

Ethan Fetaya, Shimon Ullman

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

Zhuang Ma, Yichao Lu, Dean Foster

Abstraction Selection in Model-based Reinforcement Learning

Nan Jiang, Alex Kulesza, Satinder Singh

Surrogate Functions for Maximizing Precision at the Top

Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes

Harikrishna Narasimhan, Purushottam Kar, Prateek Jain

Coresets for Nonparametric Estimation - the Case of DP-Means

Olivier Bachem, Mario Lucic, Andreas Krause

A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits

Pratik Gajane, Tanguy Urvoy, Fabrice Clérot

Functional Subspace Clustering with Application to Time Series

Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu

Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams

Rose Yu, Dehua Cheng, Yan Liu

Atomic Spatial Processes

Sean Jewell, Neil Spencer, Alexandre Bouchard-Côté

Classification with Low Rank and Missing Data

Elad Hazan, Roi Livni, Yishay Mansour

Dynamic Sensing: Better Classification under Acquisition Constraints

Oran Richman, Shie Mannor

A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis

Pinghua Gong, Jieping Ye

Telling cause from effect in deterministic linear dynamical systems

Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

Theory of Dual-sparse Regularized Randomized Reduction

Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

Generalization error bounds for learning to rank: Does the length of document lists matter?

Ambuj Tewari, Sougata Chaudhuri

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Toby Hocking, Guillem Rigaill, Guillaume Bourque

Mind the duality gap: safer rules for the Lasso

Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

A General Analysis of the Convergence of ADMM

Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Yuchen Zhang, Xiao Lin

DiSCO: Distributed Optimization for Self-Concordant Empirical Loss

Yuchen Zhang, Xiao Lin

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons

Yuxin Chen, Changho Suh

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs

Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor

Structural Maxent Models

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning

Debarghya Ghoshdastidar, Ambedkar Dukkipati

The Benefits of Learning with Strongly Convex Approximate Inference

Ben London, Bert Huang, Lise Getoor

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA

Bo Xin, David Wipf

Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View

Takanori Maehara, Akihiro Yabe, Ken-ichi Kawarabayashi

Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains

Katharina Blechschmidt, Joachim Giesen, Soeren Laue

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, Christian Szegedy

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds

Yuchen Zhang, Martin Wainwright, Michael Jordan

Landmarking Manifolds with Gaussian Processes

Dawen Liang, John Paisley

Markov Mixed Membership Models

Aonan Zhang, John Paisley

A Unified Framework for Outlier-Robust PCA-like Algorithms

Wenzhuo Yang, Huan Xu

Streaming Sparse Principal Component Analysis

Wenzhuo Yang, Huan Xu

A Divide and Conquer Framework for Distributed Graph Clustering

Wenzhuo Yang, Huan Xu

How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?

Senjian An, Farid Boussaid, Mohammed Bennamoun

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning

K. Lakshmanan, Ronald Ortner, Daniil Ryabko

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling

Michael Betancourt

Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

Dan Garber, Elad Hazan

Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models

Mrinal Das, Trapit Bansal, Chiranjib Bhattacharyya

Online Learning of Eigenvectors

Dan Garber, Elad Hazan, Tengyu Ma

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup

Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz

Ordinal Mixed Membership Models

Seppo Virtanen, Mark Girolami

Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network

Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, Alex Smola

Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares

Garvesh Raskutti, Michael Mahoney

On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence

Nathaniel Korda, Prashanth La

Learning Parametric-Output HMMs with Two Aliased States

Roi Weiss, Boaz Nadler

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Yarin Gal, Yutian Chen, Zoubin Ghahramani

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

Yarin Gal, Richard Turner

Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top

Arun Rajkumar, Suprovat Ghoshal, Lek-Heng Lim, Shivani Agarwal

Stochastic Dual Coordinate Ascent with Adaptive Probabilities

Dominik Csiba, Zheng Qu, Peter Richtarik

Vector-Space Markov Random Fields via Exponential Families

Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

Low Rank Approximation using Error Correcting Coding Matrices

Shashanka Ubaru, Arya Mazumdar, Yousef Saad

Off-policy Model-based Learning under Unknown Factored Dynamics

Assaf Hallak, Francois Schnitzler, Timothy Mann, Shie Mannor

Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification

Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen

Asymmetric Transfer Learning with Deep Gaussian Processes

Melih Kandemir

Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing

Rongda Zhu, Quanquan Gu

BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

Stephan Gouws, Yoshua Bengio, Greg Corrado

Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization

Jiangwen Sun, Jin Lu, Tingyang Xu, Jinbo Bi

Cascading Bandits: Learning to Rank in the Cascade Model

Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan

Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models

James Foulds, Shachi Kumar, Lise Getoor

Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions

Alina Ene, Huy Nguyen

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Karthik Narayan, Ali Punjani, Pieter Abbeel

Fictitious Self-Play in Extensive-Form Games

Johannes Heinrich, Marc Lanctot, David Silver

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

Adith Swaminathan, Thorsten Joachims

The Hedge Algorithm on a Continuum

Walid Krichene, Maximilian Balandat, Claire Tomlin, Alexandre Bayen

A Linear Dynamical System Model for Text

David Belanger, Sham Kakade

Unsupervised Learning of Video Representations using LSTMs

Nitish Srivastava, Elman Mansimov, Ruslan Salakhudinov

Message Passing for Collective Graphical Models

Tao Sun, Dan Sheldon, Akshat Kumar

DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics

Yining Wang, Jun Zhu

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu

MADE: Masked Autoencoder for Distribution Estimation

Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

An Online Learning Algorithm for Bilinear Models

Yuanbin Wu, Shiliang Sun

Adaptive Belief Propagation

Georgios Papachristoudis, John Fisher

Large-scale log-determinant computation through stochastic Chebyshev expansions

Insu Han, Dmitry Malioutov, Jinwoo Shin

Differentially Private Bayesian Optimization

Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger

A Nearly-Linear Time Framework for Graph-Structured Sparsity

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Support Matrix Machines

Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li

Rademacher Observations, Private Data, and Boosting

Richard Nock, Giorgio Patrini, Arik Friedman

From Word Embeddings To Document Distances

Matt Kusner, Yu Sun, Nicholas Kolkin, Kilian Weinberger

Bayesian and Empirical Bayesian Forests

Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle

Inferring Graphs from Cascades: A Sparse Recovery Framework

Jean Pouget-Abadie, Thibaut Horel

Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM

Ching-Pei Lee, Dan Roth

Safe Exploration for Optimization with Gaussian Processes

Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause

The Ladder: A Reliable Leaderboard for Machine Learning Competitions

Avrim Blum, Moritz Hardt

Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)

Maurizio Filippone, Raphael Engler

Finding Galaxies in the Shadows of Quasars with Gaussian Processes

Roman Garnett, Shirley Ho, Jeff Schneider

Following the Perturbed Leader for Online Structured Learning

Alon Cohen, Tamir Hazan

Reified Context Models

Jacob Steinhardt, Percy Liang

Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing

Yasin Abbasi-Yadkori, Peter Bartlett, Xi Chen, Alan Malek

Learning Fast-Mixing Models for Structured Prediction

Jacob Steinhardt, Percy Liang

A Probabilistic Model for Dirty Multi-task Feature Selection

Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani

On Deep Multi-View Representation Learning

Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes

Learning Program Embeddings to Propagate Feedback on Student Code

Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas

Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems

Qiang Zhou, Qi Zhao

Efficient Learning in Large-Scale Combinatorial Semi-Bandits

Zheng Wen, Branislav Kveton, Azin Ashkan

Swept Approximate Message Passing for Sparse Estimation

Andre Manoel, Florent Krzakala, Eric Tramel, Lenka Zdeborovà

Simple regret for infinitely many armed bandits

Alexandra Carpentier, Michal Valko

Exponential Integration for Hamiltonian Monte Carlo

Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei Sha

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Faster cover trees

Mike Izbicki, Christian Shelton

Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization

Tyler Johnson, Carlos Guestrin

Unsupervised Domain Adaptation by Backpropagation

Yaroslav Ganin, Victor Lempitsky

Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer

Yan-Fu Liu, Cheng-Yu Hsu, Shan-Hung Wu

Manifold-valued Dirichlet Processes

Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh

Multi-Task Learning for Subspace Segmentation

Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Tim Salimans, Diederik Kingma, Max Welling

Scalable Model Selection for Large-Scale Factorial Relational Models

Chunchen Liu, Lu Feng, Ryohei Fujimaki, Yusuke Muraoka

The Power of Randomization: Distributed Submodular Maximization on Massive Datasets

Rafael Barbosa, Alina Ene, Huy Nguyen, Justin Ward

Dealing with small data: On the generalization of context trees

Ralf Eggeling, Mikko Koivisto, Ivo Grosse

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood

Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin

A Bayesian nonparametric procedure for comparing algorithms

Alessio Benavoli, Giorgio Corani, Francesca Mangili, Marco Zaffalon

Convergence rate of Bayesian tensor estimator and its minimax optimality

Taiji Suzuki

On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments

Yifan Wu, Andras Gyorgy, Csaba Szepesvari

Nested Sequential Monte Carlo Methods

Christian Naesseth, Fredrik Lindsten, Thomas Schon

Sparse Variational Inference for Generalized GP Models

Rishit Sheth, Yuyang Wang, Roni Khardon

Universal Value Function Approximators

Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games

Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin

On Greedy Maximization of Entropy

Dravyansh Sharma, Ashish Kapoor, Amit Deshpande

Metadata Dependent Mondrian Processes

Yi Wang, Bin Li, Yang Wang, Fang Chen

Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM

Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu

Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood

Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki

Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets

Woosang Lim, Minhwan Kim, Haesun Park, Kyomin Jung

The Composition Theorem for Differential Privacy

Peter Kairouz, Sewoong Oh, Pramod Viswanath

Convex Formulation for Learning from Positive and Unlabeled Data

Marthinus Du Plessis, Gang Niu, Masashi Sugiyama

Threshold Influence Model for Allocating Advertising Budgets

Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura

Strongly Adaptive Online Learning

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

CUR Algorithm for Partially Observed Matrices

Miao Xu, Rong Jin, Zhi-Hua Zhou

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data

Yining Wang, Yu-Xiang Wang, Aarti Singh

MRA-based Statistical Learning from Incomplete Rankings

Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz

Risk and Regret of Hierarchical Bayesian Learners

Jonathan Huggins, Josh Tenenbaum

Towards a Learning Theory of Cause-Effect Inference

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin

DRAW: A Recurrent Neural Network For Image Generation

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, Daan Wierstra

Multiview Triplet Embedding: Learning Attributes in Multiple Maps

Ehsan Amid, Antti Ukkonen

Distributed Gaussian Processes

Marc Deisenroth, Jun Wei Ng

Guaranteed Tensor Decomposition: A Moment Approach

Gongguo Tang, Parikshit Shah

\(\ell_{1,p}\)-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods

Zirui Zhou, Qi Zhang, Anthony Man-Cho So

Consistent estimation of dynamic and multi-layer block models

Qiuyi Han, Kevin Xu, Edoardo Airoldi

On the Rate of Convergence and Error Bounds for LSTD(\(\lambda\))

Manel Tagorti, Bruno Scherrer

Variational Inference with Normalizing Flows

Danilo Rezende, Shakir Mohamed

Controversy in mechanistic modelling with Gaussian processes

Benn Macdonald, Catherine Higham, Dirk Husmeier

Convex Learning of Multiple Tasks and their Structure

Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco

K-hyperplane Hinge-Minimax Classifier

Margarita Osadchy, Tamir Hazan, Daniel Keren

Non-Stationary Approximate Modified Policy Iteration

Boris Lesner, Bruno Scherrer

Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees

Mathieu Serrurier, Henri Prade

Geometric Conditions for Subspace-Sparse Recovery

Chong You, Rene Vidal

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process

Amar Shah, David Knowles, Zoubin Ghahramani

Long Short-Term Memory Over Recursive Structures

Xiaodan Zhu, Parinaz Sobihani, Hongyu Guo

Weight Uncertainty in Neural Network

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra

Learning Submodular Losses with the Lovasz Hinge

Jiaqian Yu, Matthew Blaschko

Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection

Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke

Hashing for Distributed Data

Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu

Large-scale Distributed Dependent Nonparametric Trees

Zhiting Hu, Ho Qirong, Avinava Dubey, Eric Xing

Qualitative Multi-Armed Bandits: A Quantile-Based Approach

Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier

Deep Edge-Aware Filters

Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia

A Convex Optimization Framework for Bi-Clustering

Shiau Hong Lim, Yudong Chen, Huan Xu

Is Feature Selection Secure against Training Data Poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani

A Theoretical Analysis of Metric Hypothesis Transfer Learning

Michaël Perrot, Amaury Habrard

Generative Moment Matching Networks

Yujia Li, Kevin Swersky, Rich Zemel

Stay on path: PCA along graph paths

Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran

Deep Learning with Limited Numerical Precision

Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan

Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices

Jie Wang, Jieping Ye

Harmonic Exponential Families on Manifolds

Taco Cohen, Max Welling

Training Deep Convolutional Neural Networks to Play Go

Christopher Clark, Amos Storkey

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

Andrew Wilson, Hannes Nickisch

Learning Deep Structured Models

Liang-Chieh Chen, Alexander Schwing, Alan Yuille, Raquel Urtasun

Community Detection Using Time-Dependent Personalized PageRank

Haim Avron, Lior Horesh

Scalable Variational Inference in Log-supermodular Models

Josip Djolonga, Andreas Krause

Variational Inference for Gaussian Process Modulated Poisson Processes

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts

Scalable Deep Poisson Factor Analysis for Topic Modeling

Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin

Hidden Markov Anomaly Detection

Nico Goernitz, Mikio Braun, Marius Kloft

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes

Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo

Convex Calibrated Surrogates for Hierarchical Classification

Harish Ramaswamy, Ambuj Tewari, Shivani Agarwal

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Jose Miguel Hernandez-Lobato, Ryan Adams

Active Nearest Neighbors in Changing Environments

Christopher Berlind, Ruth Urner

Bipartite Edge Prediction via Transductive Learning over Product Graphs

Hanxiao Liu, Yiming Yang

Trust Region Policy Optimization

John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz

Discovering Temporal Causal Relations from Subsampled Data

Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit Dhillon

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing

On Symmetric and Asymmetric LSHs for Inner Product Search

Behnam Neyshabur, Nathan Srebro

The Kendall and Mallows Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert

Bayesian Multiple Target Localization

Purnima Rajan, Weidong Han, Raphael Sznitman, Peter Frazier, Bruno Jedynak

Submodularity in Data Subset Selection and Active Learning

Kai Wei, Rishabh Iyer, Jeff Bilmes

Variational Generative Stochastic Networks with Collaborative Shaping

Philip Bachman, Doina Precup

Adding vs. Averaging in Distributed Primal-Dual Optimization

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac

Feature-Budgeted Random Forest

Feng Nan, Joseph Wang, Venkatesh Saligrama

Entropic Graph-based Posterior Regularization

Maxwell Libbrecht, Michael Hoffman, Jeff Bilmes, William Noble

Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations

Tam Le, Marco Cuturi

Low-Rank Matrix Recovery from Row-and-Column Affine Measurements

Or Zuk, Avishai Wagner

Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction

Sébastien Giguère, Amélie Rolland, Francois Laviolette, Mario Marchand

A Multitask Point Process Predictive Model

Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin

A Hybrid Approach for Probabilistic Inference using Random Projections

Michael Zhu, Stefano Ermon

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio

Learning to Search Better than Your Teacher

Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume, John Langford

Gated Feedback Recurrent Neural Networks

Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio

Context-based Unsupervised Data Fusion for Decision Making

Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar

Phrase-based Image Captioning

Remi Lebret, Pedro Pinheiro, Ronan Collobert

Celeste: Variational inference for a generative model of astronomical images

Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Mr Prabhat

Distributional Rank Aggregation, and an Axiomatic Analysis

Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar

Gradient-based Hyperparameter Optimization through Reversible Learning

Dougal Maclaurin, David Duvenaud, Ryan Adams

Bimodal Modelling of Source Code and Natural Language

Miltos Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei

Cheap Bandits

Manjesh Hanawal, Venkatesh Saligrama, Michal Valko, Remi Munos

Subsampling Methods for Persistent Homology

Frederic Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry Wasserman

An embarrassingly simple approach to zero-shot learning

Bernardino Romera-Paredes, Philip Torr

Binary Embedding: Fundamental Limits and Fast Algorithm

Xinyang Yi, Constantine Caramanis, Eric Price

Scalable Bayesian Optimization Using Deep Neural Networks

Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, Ryan Adams

How Hard is Inference for Structured Prediction?

Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

Online Time Series Prediction with Missing Data

Oren Anava, Elad Hazan, Assaf Zeevi

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach

Jason Pacheco, Erik Sudderth

A Fast Variational Approach for Learning Markov Random Field Language Models

Yacine Jernite, Alexander Rush, David Sontag

Removing systematic errors for exoplanet search via latent causes

Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

Yves-Laurent Kom Samo, Stephen Roberts

Correlation Clustering in Data Streams

KookJin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, Anthony Wirth

Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

Qingming Tang, Siqi Sun, Jinbo Xu

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli

Modeling Order in Neural Word Embeddings at Scale

Andrew Trask, David Gilmore, Matthew Russell

Distributed Inference for Dirichlet Process Mixture Models

Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani

Compressing Neural Networks with the Hashing Trick

Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen

Intersecting Faces: Non-negative Matrix Factorization With New Guarantees

Rong Ge, James Zou

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix

Roger Grosse, Ruslan Salakhudinov

A Deeper Look at Planning as Learning from Replay

Harm Vanseijen, Rich Sutton

Optimal and Adaptive Algorithms for Online Boosting

Alina Beygelzimer, Satyen Kale, Haipeng Luo

Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems

Christopher De Sa, Christopher Re, Kunle Olukotun

An Empirical Exploration of Recurrent Network Architectures

Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever

Complete Dictionary Recovery Using Nonconvex Optimization

Ju Sun, Qing Qu, John Wright

Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

Haitham Bou Ammar, Rasul Tutunov, Eric Eaton

PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent

Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

High Confidence Policy Improvement

Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh

Fixed-point algorithms for learning determinantal point processes

Zelda Mariet, Suvrit Sra

Consistent Multiclass Algorithms for Complex Performance Measures

Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha, Shivani Agarwal

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

James Martens, Roger Grosse

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models

En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon

Multi-instance multi-label learning in the presence of novel class instances

Anh Pham, Raviv Raich, Xiaoli Fern, Jesús Pérez Arriaga

Entropy-Based Concentration Inequalities for Dependent Variables

Liva Ralaivola, Massih-Reza Amini

PU Learning for Matrix Completion

Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization

Necdet Aybat, Zi Wang, Garud Iyengar

Sparse Subspace Clustering with Missing Entries

Congyuan Yang, Daniel Robinson, Rene Vidal

Moderated and Drifting Linear Dynamical Systems

Jinyan Guan, Kyle Simek, Ernesto Brau, Clayton Morrison, Emily Butler, Kobus Barnard

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions

Taehoon Lee, Sungroh Yoon

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

Yu-Xiang Wang, Stephen Fienberg, Alex Smola

A trust-region method for stochastic variational inference with applications to streaming data

Lucas Theis, Matt Hoffman

Inference in a Partially Observed Queuing Model with Applications in Ecology

Kevin Winner, Garrett Bernstein, Dan Sheldon

Deterministic Independent Component Analysis

Ruitong Huang, Andras Gyorgy, Csaba Szepesvári

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property

Maxime Gasse, Alexandre Aussem, Haytham Elghazel

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

Roy Frostig, Rong Ge, Sham Kakade, Aaron Sidford

A New Generalized Error Path Algorithm for Model Selection

Bin Gu, Charles Ling