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JMLR Workshop and Conference Proceedings

Volume 51: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics

Editors: Arthur Gretton, Christian C. Robert


Accepted Papers

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Mario Lucic, Olivier Bachem, Andreas Krause

Revealing Graph Bandits for Maximizing Local Influence

Alexandra Carpentier, Michal Valko

Convex Block-sparse Linear Regression with Expanders – Provably

Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman

Clamping Improves TRW and Mean Field Approximations

Adrian Weller, Justin Domke

Tightness of LP Relaxations for Almost Balanced Models

Adrian Weller, Mark Rowland, David Sontag

Control Functionals for Quasi-Monte Carlo Integration

Chris Oates, Mark Girolami

Probability Inequalities for Kernel Embeddings in Sampling without Replacement

Markus Schneider

Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking

Nicolas Goix, Anne Sabourin, Stéphan Clémençon

A Robust-Equitable Copula Dependence Measure for Feature Selection

Yale Chang, Yi Li, Adam Ding, Jennifer Dy

Random Forest for the Contextual Bandit Problem

Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot

Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics

Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard

Learning Sparse Additive Models with Interactions in High Dimensions

Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

Bipartite Correlation Clustering: Maximizing Agreements

Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis

Breaking Sticks and Ambiguities with Adaptive Skip-gram

Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov

Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls

Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu

Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices

Jonathan Scarlett, Volkan Cevher

Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models

Lee H. Dicker, Murat A. Erdogdu

Scalable Gaussian Process Classification via Expectation Propagation

Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates

Lingxiao Wang, Xiang Ren, Quanquan Gu

On the Reducibility of Submodular Functions

Jincheng Mei, Hao Zhang, Bao-Liang Lu

Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

Atsushi Nitanda

Fast Convergence of Online Pairwise Learning Algorithms

Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön

Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables

Yaniv Tenzer, Gal Elidan

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes

Alexander G. de G. Matthews, James Hensman, Richard Turner, Zoubin Ghahramani

Non-stochastic Best Arm Identification and Hyperparameter Optimization

Kevin Jamieson, Ameet Talwalkar

A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz, Robert Nishihara, Michael Jordan

No Regret Bound for Extreme Bandits

Robert Nishihara, David Lopez-Paz, Leon Bottou

Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations

Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan

Online Learning to Rank with Feedback at the Top

Sougata Chaudhuri, Ambuj Tewari Tewari

Survey Propagation beyond Constraint Satisfaction Problems

Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey

Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models

Balázs Csanád Csáji

CRAFT: ClusteR-specific Assorted Feature selecTion

Vikas K. Garg, Cynthia Rudin, Tommi Jaakkola

Time-Varying Gaussian Process Bandit Optimization

Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher

Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

Weici Hu, Peter Frazier

Bayesian Markov Blanket Estimation

Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth

Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen

Unsupervised Ensemble Learning with Dependent Classifiers

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger

Multi-Level Cause-Effect Systems

Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona

Deep Kernel Learning

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

Nearly Optimal Classification for Semimetrics

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Latent Point Process Allocation

Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings

Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg

A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning

Yan Li, Han Liu, Warren Powell

Optimal Statistical and Computational Rates for One Bit Matrix Completion

Renkun Ni, Quanquan Gu

PAC-Bayesian Bounds based on the Rényi Divergence

Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

Simple and Scalable Constrained Clustering: a Generalized Spectral Method

Mihai Cucuringu, Ioannis Koutis, Sanjay Chawla, Gary Miller, Richard Peng

Geometry Aware Mappings for High Dimensional Sparse Factors

Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu

Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA

Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu

Quantization based Fast Inner Product Search

Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha

An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

Learning Structured Low-Rank Representation via Matrix Factorization

Jie Shen, Ping Li

A PAC RL Algorithm for Episodic POMDPs

Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

Sujith Ravi, Qiming Diao

Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models

Calvin McCarter, Seyoung Kim

Graph Connectivity in Noisy Sparse Subspace Clustering

Yining Wang, Yu-Xiang Wang, Aarti Singh

The Nonparametric Kernel Bayes Smoother

Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song

Universal Models of Multivariate Temporal Point Processes

Asela Gunawardana, Chris Meek

Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings

Zhitang Chen, Pascal Poupart, Yanhui Geng

Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines

Muneki Yasuda

Enumerating Equivalence Classes of Bayesian Networks using EC Graphs

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

Low-Rank and Sparse Structure Pursuit via Alternating Minimization

Quanquan Gu, Zhaoran Wang Wang, Han Liu

NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning

Eli Meirom, Pavel Kisilev

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization

Fanhua Shang, Yuanyuan Liu, James Cheng

Fast Dictionary Learning with a Smoothed Wasserstein Loss

Antoine Rolet, Marco Cuturi, Gabriel Peyré

New Resistance Distances with Global Information on Large Graphs

Canh Hao Nguyen, Hiroshi Mamitsuka

Batch Bayesian Optimization via Local Penalization

Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence

Nonparametric Budgeted Stochastic Gradient Descent

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

Learning Relationships between Data Obtained Independently

Alexandra Carpentier, Teresa Schlueter

Fast and Scalable Structural SVM with Slack Rescaling

Heejin Choi, Ofer Meshi, Nathan Srebro

Probabilistic Approximate Least-Squares

Simon Bartels, Philipp Hennig

Approximate Inference Using DC Programming For Collective Graphical Models

Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Sequential Inference for Deep Gaussian Process

Yali Wang, Marcus Brubaker, Brahim Chaib-Draa, Raquel Urtasun

Variational Tempering

Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System

Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing

Scalable MCMC for Mixed Membership Stochastic Blockmodels

Wenzhe Li, Sungjin Ahn, Max Welling

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

A Deep Generative Deconvolutional Image Model

Yunchen Pu, Win Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin

Distributed Multi-Task Learning

Jialei Wang, Mladen Kolar, Nathan Srerbo

A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models

Rishit Sheth, Roni Khardon

Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation

Sebastian Tschiatschek, Josip Djolonga, Andreas Krause

Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm

Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan

GLASSES: Relieving The Myopia Of Bayesian Optimisation

Javier Gonzalez, Michael Osborne, Neil Lawrence

Stochastic Variational Inference for the HDP-HMM

Aonan Zhang, San Gultekin, John Paisley

Stochastic Neural Networks with Monotonic Activation Functions

Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvari

Variational Gaussian Copula Inference

Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin

Low-Rank Approximation of Weighted Tree Automata

Guillaume Rabusseau, Borja Balle, Shay Cohen

Accelerating Online Convex Optimization via Adaptive Prediction

Mehryar Mohri, Scott Yang

Scalable geometric density estimation

Ye Wang, Antonio Canale, David Dunson

Model-based Co-clustering for High Dimensional Sparse Data

Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

Christina Heinze, Brian McWilliams, Nicolai Meinshausen

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games

Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin

Semi-Supervised Learning with Adaptive Spectral Transform

Hanxiao Liu, Yiming Yang

Pseudo-Marginal Slice Sampling

Iain Murray, Matthew Graham

How to Learn a Graph from Smooth Signals

Vassilis Kalofolias

Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects

Mario Figueiredo, Robert Nowak

Pareto Front Identification from Stochastic Bandit Feedback

Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan

Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces

Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian

AdaDelay: Delay Adaptive Distributed Stochastic Optimization

Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola

Exponential Stochastic Cellular Automata for Massively Parallel Inference

Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele

Globally Sparse Probabilistic PCA

Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, Le Song

Unsupervised Feature Selection by Preserving Stochastic Neighbors

Xiaokai Wei, Philip S. Yu

Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing

Optimization as Estimation with Gaussian Processes in Bandit Settings

Zi Wang, Bolei Zhou, Stefanie Jegelka

A Convex Surrogate Operator for General Non-Modular Loss Functions

Jiaqian Yu, Matthew Blaschko

Inference for High-dimensional Exponential Family Graphical Models

Jialei Wang, Mladen Kolar

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin

Fitting Spectral Decay with the \(k\)-Support Norm

Andrew McDonald, Massimiliano Pontil, Dimitris Stamos

Early Stopping as Nonparametric Variational Inference

David Duvenaud, Dougal Maclaurin, Ryan Adams

Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing

Tight Variational Bounds via Random Projections and I-Projections

Lun-Kai Hsu, Tudor Achim, Stefano Ermon

Bethe Learning of Graphical Models via MAP Decoding

Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara

Determinantal Regularization for Ensemble Variable Selection

Veronika Rockova, Gemma Moran, Edward George

Scalable and Sound Low-Rank Tensor Learning

Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans

Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information

Changwei Hu, Piyush Rai, Lawrence Carin

Topic-Based Embeddings for Learning from Large Knowledge Graphs

Changwei Hu, Piyush Rai, Lawrence Carin

Consistently Estimating Markov Chains with Noisy Aggregate Data

Garrett Bernstein, Daniel Sheldon

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

Improper Deep Kernels

Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson

Unbounded Bayesian Optimization via Regularization

Bobak Shahriari, Alexandre Bouchard-Cote, Nando de Freitas

Non-Gaussian Component Analysis with Log-Density Gradient Estimation

Hiroaki Sasaki, Gang Niu, Masashi Sugiyama

Online Learning with Noisy Side Observations

Tomáš Kocák, Gergely Neu, Michal Valko

Black-Box Policy Search with Probabilistic Programs

Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood

Efficient Bregman Projections onto the Permutahedron and Related Polytopes

Cong Han Lim, Stephen J. Wright

On Searching for Generalized Instrumental Variables

Benito Van der Zander, Maciej Liśkiewicz

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

Hanie Sedghi, Majid Janzamin, Anima Anandkumar

Controlling Bias in Adaptive Data Analysis Using Information Theory

Daniel Russo, James Zou

A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees

Jean-Francis Roy, Mario Marchand, François Laviolette

Graph Sparsification Approaches for Laplacian Smoothing

Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani

Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation

Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar

Robust Covariate Shift Regression

Xiangli Chen, Mathew Monfort, Anqi Liu, Brian D. Ziebart

On Lloyd’s Algorithm: New Theoretical Insights for Clustering in Practice

Cheng Tang, Claire Monteleoni

Towards Stability and Optimality in Stochastic Gradient Descent

Panos Toulis, Dustin Tran, Edo Airoldi

Communication Efficient Distributed Agnostic Boosting

Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau

Private Causal Inference

Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger

Parallel Markov Chain Monte Carlo via Spectral Clustering

Guillaume Basse, Aaron Smith, Natesh Pillai

Efficient Sampling for k-Determinantal Point Processes

Chengtao Li, Stefanie Jegelka, Suvrit Sra

A Fast and Reliable Policy Improvement Algorithm

Yasin Abbasi-Yadkori, Peter L. Bartlett, Stephen J. Wright

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin

Active Learning Algorithms for Graphical Model Selection

Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong H. Park

Streaming Kernel Principal Component Analysis

Mina Ghashami, Daniel J. Perry, Jeff Phillips

Back to the Future: Radial Basis Function Networks Revisited

Qichao Que, Mikhail Belkin

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions

Loic Landrieu, Guillaume Obozinski

Loss Bounds and Time Complexity for Speed Priors

Daniel Filan, Jan Leike, Marcus Hutter

NYTRO: When Subsampling Meets Early Stopping

Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco

Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments

Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert

Spectral M-estimation with Applications to Hidden Markov Models

Dustin Tran, Minjae Kim, Finale Doshi-Velez

Chained Gaussian Processes

Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

Multiresolution Matrix Compression

Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor

Supervised Neighborhoods for Distributed Nonparametric Regression

Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu

Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

Dejiao Zhang, Laura Balzano

Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks

Abdullah Rashwan, Han Zhao, Pascal Poupart

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees

Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani

Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization

Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin

Discriminative Structure Learning of Arithmetic Circuits

Amirmohammad Rooshenas, Daniel Lowd

One Scan 1-Bit Compressed Sensing

Ping Li