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JMLR Workshop and Conference Proceedings

Volume 38: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics

Editors: Guy Lebanon, S.V.N. Vishwanathan

Contents:

Accepted Papers

Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices

Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou

Parameter Estimation of Generalized Linear Models without Assuming their Link Function

Sreangsu Acharyya, Joydeep Ghosh

Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method

David Anderson, Simon Du, Michael Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu

Global Multi-armed Bandits with Hölder Continuity

Onur Atan, Cem Tekin, Mihaela van der Schaar

Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures

Martin Azizyan, Aarti Singh, Larry Wasserman

Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees

Stephen Bach, Bert Huang, Lise Getoor

Near-optimal max-affine estimators for convex regression

Gabor Balazs, András György, Csaba Szepesvari

Convex Multi-Task Learning by Clustering

Aviad Barzilai, Koby Crammer

Gaussian Processes for Bayesian hypothesis tests on regression functions

Alessio Benavoli, Francesca Mangili

Sparse Solutions to Nonnegative Linear Systems and Applications

Aditya Bhaskara, Ananda Suresh, Morteza Zadimoghaddam

Generalized Linear Models for Aggregated Data

Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

Accurate and conservative estimates of MRF log-likelihood using reverse annealing

Yuri Burda, Roger Grosse, Ruslan Salakhutdinov

Stochastic Spectral Descent for Restricted Boltzmann Machines

David Carlson, Volkan Cevher, Lawrence Carin

Implementable confidence sets in high dimensional regression

Alexandra Carpentier

Online Ranking with Top-1 Feedback

Sougata Chaudhuri, Ambuj Tewari

One-bit Compressed Sensing with the k-Support Norm

Sheng Chen, Arindam Banerjee

Efficient Second-Order Gradient Boosting for Conditional Random Fields

Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin

Filtered Search for Submodular Maximization with Controllable Approximation Bounds

Wenlin Chen, Yixin Chen, Kilian Weinberger

Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems

Xiangli Chen, Brian Ziebart

Exact Bayesian Learning of Ancestor Relations in Bayesian Networks

Yetian Chen, Lingjian Meng, Jin Tian

Model Selection for Topic Models via Spectral Decomposition

Dehua Cheng, Xinran He, Yan Liu

The Loss Surfaces of Multilayer Networks

Anna Choromanska, MIkael Henaff, Michael Mathieu, Gerard Ben Arous, Yann LeCun

Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions

Alexandre Defossez, Francis Bach

A Topic Modeling Approach to Ranking

Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

A totally unimodular view of structured sparsity

Marwa El Halabi, Volkan Cevher

Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades

Mehrdad Farajtabar, Manuel Gomez Rodriguez, Mohammad Zamani, Nan Du, Hongyuan Zha, Le Song

Graph Approximation and Clustering on a Budget

Ethan Fetaya, Ohad Shamir, Shimon Ullman

A Sufficient Statistics Construction of Exponential Family Le ́vy Measure Densities for Nonparametric Conjugate Models

Robert Finn, Brian Kulis

Computational Complexity of Linear Large Margin Classification With Ramp Loss

Søren Frejstrup Maibing, Christian Igel

Learning Deep Sigmoid Belief Networks with Data Augmentation

Zhe Gan, Ricardo Henao, David Carlson, Lawrence Carin

Efficient Estimation of Mutual Information for Strongly Dependent Variables

Shuyang Gao, Greg Ver Steeg, Aram Galstyan

On Anomaly Ranking and Excess-Mass Curves

Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Modeling Skill Acquisition Over Time with Sequence and Topic Modeling

José González-Brenes

Consistent Collective Matrix Completion under Joint Low Rank Structure

Suriya Gunasekar, Makoto Yamada, Dawei Yin, Yi Chang

The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation

Fangjian Guo, Charles Blundell, Hanna Wallach, Katherine Heller

Preserving Privacy of Continuous High-dimensional Data with Minimax Filters

Jihun Hamm

A Consistent Method for Graph Based Anomaly Localization

Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki

Metric recovery from directed unweighted graphs

Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

Scalable Variational Gaussian Process Classification

James Hensman, Alexander Matthews, Zoubin Ghahramani

Stochastic Structured Variational Inference

Matthew Hoffman, David Blei

Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process

Michael Hughes, Dae Il Kim, Erik Sudderth

Cross-domain recommendation without shared users or items by sharing latent vector distributions

Tomoharu Iwata, Takeuchi Koh

Submodular Point Processes with Applications to Machine learning

Rishabh Iyer, Jeffrey Bilmes

Online Optimization : Competing with Dynamic Comparators

Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan

Estimating the accuracies of multiple classifiers without labeled data

Ariel Jaffe, Boaz Nadler, Yuval Kluger

Sparse Dueling Bandits

Kevin Jamieson, Sumeet Katariya, Atul Deshpande, Robert Nowak

Consensus Message Passing for Layered Graphical Models

Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

Robust Cost Sensitive Support Vector Machine

Shuichi Katsumata, Akiko Takeda

On Approximate Non-submodular Minimization via Tree-Structured Supermodularity

Yoshinobu Kawahara, Rishabh Iyer, Jeffrey Bilmes

Sparse Submodular Probabilistic PCA

Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo

Latent feature regression for multivariate count data

Arto Klami, Abhishek Tripathi, Johannes Sirola, Lauri Väre, Frederic Roulland

Dimensionality estimation without distances

Matthäus Kleindessner, Ulrike von Luxburg

A Bayes consistent 1-NN classifier

Aryeh Kontorovich, Roi Weiss

DART: Dropouts meet Multiple Additive Regression Trees

Rashmi Korlakai Vinayak, Ran Gilad-Bachrach

On Estimating L22 Divergence

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

Tensor Factorization via Matrix Factorization

Volodymyr Kuleshov, Arun Chaganty, Percy Liang

Low-Rank Spectral Learning with Weighted Loss Functions

Alex Kulesza, Nan Jiang, Satinder Singh

Symmetric Iterative Proportional Fitting

Sven Kurras

Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering

Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach

Particle Gibbs for Bayesian Additive Regression Trees

Balaji Lakshminarayanan, Daniel Roy, Yee Whye Teh

Deeply-Supervised Nets

Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu

Preferential Attachment in Graphs with Affinities

Jay Lee, Manzil Zaheer, Stephan Günnemann, Alex Smola

Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility

Juho Lee, Seungjin Choi

Modelling Policies in MDPs in Reproducing Kernel Hilbert Space

Guy Lever, Ronnie Stafford

Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings

Bo Li, Yevgeniy Vorobeychik

Toward Minimax Off-policy Value Estimation

Lihong Li, Remi Munos, Csaba Szepesvari

Compressed Sensing with Very Sparse Gaussian Random Projections

Ping Li, Cun-Hui Zhang

Max-Margin Zero-Shot Learning for Multi-class Classification

Xin Li, Yuhong Guo

Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels

Xin Li, Feipeng Zhao, Yuhong Guo

Sparsistency of 1-Regularized M-Estimators

Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher

Similarity Learning for High-Dimensional Sparse Data

Kuan Liu, Aurélien Bellet, Fei Sha

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

Mario Lucic, Mesrob Ohannessian, Amin Karbasi, Andreas Krause

Active Pointillistic Pattern Search

Yifei Ma, Dougal Sutherland, Roman Garnett, Jeff Schneider

The Security of Latent Dirichlet Allocation

Shike Mei, Xiaojin Zhu

A Spectral Algorithm for Inference in Hidden semi-Markov Models

Igor Melnyk, Arindam Banerjee

Efficient Training of Structured SVMs via Soft Constraints

Ofer Meshi, Nathan Srebro, Tamir Hazan

Variance Reduction via Antithetic Markov Chains

James Neufeld, Dale Schuurmans, Michael Bowling

Fast Function to Function Regression

Junier Oliva, William Neiswanger, Barnabas Poczos, Eric Xing, Hy Trac, Shirley Ho, Jeff Schneider

Reactive bandits with attitude

Pedro Ortega, Kee-Eung Kim, Daniel Lee

Feature Selection for Linear SVM with Provable Guarantees

Saurabh Paul, Malik Magdon-Ismail, Petros Drineas

On Theoretical Properties of Sum-Product Networks

Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro Domingos

Robust sketching for multiple square-root LASSO problems

Vu Pham, Laurent El Ghaoui

Deep Exponential Families

Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David Blei

On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives

Sashank Reddi, Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman

A Scalable Algorithm for Structured Kernel Feature Selection

Shaogang Ren, Shuai Huang, John Onofrey, Xenios Papademetris, Xiaoning Qian

Learning Efficient Anomaly Detectors from K-NN Graphs

Jonathan Root, Jing Qian, Venkatesh Saligrama

Gamma Processes, Stick-Breaking, and Variational Inference

Anirban Roychowdhury, Brian Kulis

Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation

Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

Mark Schmidt, Reza Babanezhad, Mohamed Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar

Sensor Selection for Crowdsensing Dynamical Systems

Francois Schnitzler, Jia Yuan Yu, Shie Mannor

A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels

Clayton Scott

Inference of Cause and Effect with Unsupervised Inverse Regression

Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf

Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin Wainwright

Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM

Roshan Shariff, András György, Csaba Szepesvari

Learning Where to Sample in Structured Prediction

Tianlin Shi, Jacob Steinhardt, Percy Liang

State Space Methods for Efficient Inference in Student-t Process Regression

Arno Solin, Simo Särkkä

Learning from Data with Heterogeneous Noise using SGD

Shuang Song, Kamalika Chaudhuri, Anand Sarwate

Data modeling with the elliptical gamma distribution

Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge

WASP: Scalable Bayes via barycenters of subset posteriors

Sanvesh Srivastava, Volkan Cevher, Quoc Dinh, David Dunson

Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

Julien Stoehr, Nial Friel

A Dirichlet Process Mixture Model for Spherical Data

Julian Straub, Jason Chang, Oren Freifeld, John Fisher III

Inferring Block Structure of Graphical Models in Exponential Families

Siqi Sun, Hai Wang, Jinbo Xu

Two-stage sampled learning theory on distributions

Zoltan Szabo, Arthur Gretton, Barnabas Poczos, Bharath Sriperumbudur

Predicting Preference Reversals via Gaussian Process Uncertainty Aversion

Rikiya Takahashi, Tetsuro Morimura

Streaming Variational Inference for Bayesian Nonparametric Mixture Models

Alex Tank, Nicholas Foti, Emily Fox

Missing at Random in Graphical Models

Jin Tian

Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood

Learning of Non-Parametric Control Policies with High-Dimensional State Features

Herke Van Hoof, Jan Peters, Gerhard Neumann

Maximally Informative Hierarchical Representations of High-Dimensional Data

Greg Ver Steeg, Aram Galstyan

Falling Rule Lists

Fulton Wang, Cynthia Rudin

Multi-Manifold Modeling in Non-Euclidean spaces

Xu Wang, Konstantinos Slavakis, Gilad Lerman

Column Subset Selection with Missing Data via Active Sampling

Yining Wang, Aarti Singh

Trend Filtering on Graphs

Yu-Xiang Wang, James Sharpnack, Alex Smola, Ryan Tibshirani

A Greedy Homotopy Method for Regression with Nonconvex Constraints

Fabian Wauthier, Peter Donnelly

Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs

Adrian Weller

Understanding and Evaluating Sparse Linear Discriminant Analysis

Yi Wu, David Wipf, Jeong-Min Yun

Stochastic Block Transition Models for Dynamic Networks

Kevin Xu

Majorization-Minimization for Manifold Embedding

Zhirong Yang, Jaakko Peltonen, Samuel Kaski

A la Carte – Learning Fast Kernels

Zichao Yang, Andrew Wilson, Alex Smola, Le Song

Minimizing Nonconvex Non-Separable Functions

Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric Xing

A Simple Homotopy Algorithm for Compressive Sensing

Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

Scalable Nonparametric Multiway Data Analysis

Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi, Youngja Park

Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

Mingyuan Zhou

Power-Law Graph Cuts

Xiangyang Zhou, Jiaxin Zhang, Brian Kulis

The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models

Yuancheng Zhu, Rina Foygel Barber