Preface

Samuel Kaski, Jukka Corander; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:i-iv

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Decontamination of Mutually Contaminated Models

Gilles Blanchard, Clayton Scott; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1-9

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Distributed optimization of deeply nested systems

Miguel Carreira-Perpinan, Weiran Wang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:10-19

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Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?

Shinichi Nakajima, Masashi Sugiyama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:20-28

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Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes

Nir Ailon; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:29-37

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Information-Theoretic Characterization of Sparse Recovery

Cem Aksoylar, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:38-46

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Hybrid Discriminative-Generative Approach with Gaussian Processes

Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:47-56

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Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs

Waheed Bajwa, Marco Duarte, Robert Calderbank; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:57-67

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A New Perspective on Learning Linear Separators with Large L_qL_p Margins

Maria-Florina Balcan, Christopher Berlind; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:68-76

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A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response

Ava Bargi, Richard Yi Xu, Zoubin Ghahramani, Massimo Piccardi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:77-85

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Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability

Jeremias Berg, Matti Järvisalo, Brandon Malone; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:86-95

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Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion

Mathieu Blondel, Yotaro Kubo, Ueda Naonori; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:96-104

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PAC-Bayesian Theory for Transductive Learning

Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:105-113

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Random Bayesian networks with bounded indegree

Eunice Yuh-Jie Chen, Judea Pearl; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:114-121

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Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs

Jianhui Chen, Tianbao Yang, Shenghuo Zhu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:122-130

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Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems

Robert Cohn, Satinder Singh, Edmund Durfee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:131-139

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Doubly Aggressive Selective Sampling Algorithms for Classification

Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:140-148

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Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus

Vinny Davies, Richard Reeve, William Harvey, Francois Maree, Dirk Husmeier; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:149-158

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Sparsity and the Truncated $l^2$-norm

Lee Dicker; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:159-166

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Efficient Distributed Topic Modeling with Provable Guarantees

Weicong Ding, Mohammad Rohban, Prakash Ishwar, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:167-175

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Pan-sharpening with a Bayesian nonparametric dictionary learning model

Xinghao Ding, Yiyong Jiang, Yue Huang, John Paisley; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:176-184

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Approximate Slice Sampling for Bayesian Posterior Inference

Christopher DuBois, Anoop Korattikara, Max Welling, Padhraic Smyth; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:185-193

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Bayesian Logistic Gaussian Process Models for Dynamic Networks

Daniele Durante, David Dunson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:194-201

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Avoiding pathologies in very deep networks

David Duvenaud, Oren Rippel, Ryan Adams, Zoubin Ghahramani; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:202-210

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Efficient Inference for Complex Queries on Complex Distributions

Lili Dworkin, Michael Kearns, Lirong Xia; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:211-219

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Bayesian Switching Interaction Analysis Under Uncertainty

Zoran Dzunic, John Fisher III; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:220-228

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Robust learning of inhomogeneous PMMs

Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:229-237

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Fully-Automatic Bayesian Piecewise Sparse Linear Models

Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:238-246

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Learning with Maximum A-Posteriori Perturbation Models

Andreea Gane, Tamir Hazan, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:247-256

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Sketching the Support of a Probability Measure

Joachim Giesen, Soeren Laue, Lars Kuehne; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:257-265

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Robust Stochastic Principal Component Analysis

John Goes, Teng Zhang, Raman Arora, Gilad Lerman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:266-274

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Bayesian Nonparametric Poisson Factorization for Recommendation Systems

Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David Blei; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:275-283

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Efficiently Enforcing Diversity in Multi-Output Structured Prediction

Abner Guzman-Rivera, Pushmeet Kohli, Dhruv Batra, Rob Rutenbar; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:284-292

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Learning and Evaluation in Presence of Non-i.i.d. Label Noise

Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio Braun, Klaus-Robert Mueller, Marius Kloft; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:293-302

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Analytic Long-Term Forecasting with Periodic Gaussian Processes

Nooshin HajiGhassemi, Marc Deisenroth; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:303-311

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On Estimating Causal Effects based on Supplemental Variables

Takahiro Hayashi, Manabu Kuroki; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:312-319

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Non-Asymptotic Analysis of Relational Learning with One Network

Peng He, Changshui Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:320-327

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Exploiting the Limits of Structure Learning via Inherent Symmetry

Peng He, Changshui Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:328-337

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A Statistical Model for Event Sequence Data

Kevin Heins, Hal Stern; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:338-346

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Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Philipp Hennig, Søren Hauberg; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:347-355

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Tilted Variational Bayes

James Hensman, Max Zwiessele, Neil D. Lawrence; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:356-364

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On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

Matthew Hoffman, Bobak Shahriari, Nando Freitas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:365-374

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Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors

Junya Honda, Akimichi Takemura; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:375-383

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Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees

Jean Honorio, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:384-392

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Latent Gaussian Models for Topic Modeling

Changwei Hu, Eunsu Ryu, David Carlson, Yingjian Wang, Lawrence Carin; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:393-401

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A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models

Ruitong Huang, Csaba Szepesvari; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:402-410

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Global Optimization Methods for Extended Fisher Discriminant Analysis

Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:411-419

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High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation

Rafael Izbicki, Ann Lee, Chad Schafer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:420-429

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Near Optimal Bayesian Active Learning for Decision Making

Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha Srinivasa; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:430-438

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A Level-set Hit-and-run Sampler for Quasi-Concave Distributions

Shane Jensen, Dean Foster; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:439-447

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New Bounds on Compressive Linear Least Squares Regression

Ata Kaban; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:448-456

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Recovering Distributions from Gaussian RKHS Embeddings

Motonobu Kanagawa, Kenji Fukumizu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:457-465

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Collaborative Ranking for Local Preferences

Berk Kapicioglu, David Rosenberg, Robert Schapire, Tony Jebara; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:466-474

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Scalable Collaborative Bayesian Preference Learning

Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:475-483

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A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data

Do-kyum Kim, Matthew Der, Lawrence Saul; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:484-492

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Scalable Variational Bayesian Matrix Factorization with Side Information

Yong-Deok Kim, Seungjin Choi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:493-502

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Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold

Franz Király, Martin Ehler; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:503-511

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Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

Jyri Kivinen, Chris Williams, Nicolas Heess; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:512-521

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Low-Rank Spectral Learning

Alex Kulesza, N. Raj Rao, Satinder Singh; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:522-530

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Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data

Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:531-539

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Computational Education using Latent Structured Prediction

Tanja Käser, Alexander Schwing, Tamir Hazan, Markus Gross; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:540-548

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Towards building a Crowd-Sourced Sky Map

Dustin Lang, David Hogg, Bernhard Schölkopf; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:549-557

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Incremental Tree-Based Inference with Dependent Normalized Random Measures

Juho Lee, Seungjin Choi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:558-566

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Jointly Informative Feature Selection

Leonidas Lefakis, Francois Fleuret; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:567-575

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Learning Heterogeneous Hidden Markov Random Fields

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:576-584

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PAC-Bayesian Collective Stability

Ben London, Bert Huang, Ben Taskar, Lise Getoor; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:585-594

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Active Area Search via Bayesian Quadrature

Yifei Ma, Roman Garnett, Jeff Schneider; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:595-603

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Active Boundary Annotation using Random MAP Perturbations

Subhransu Maji, Tamir Hazan, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:604-613

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Interpretable Sparse High-Order Boltzmann Machines

Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:614-622

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Efficient Lifting of MAP LP Relaxations Using k-Locality

Martin Mladenov, Kristian Kersting, Amir Globerson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:623-632

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A Geometric Algorithm for Scalable Multiple Kernel Learning

John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:633-642

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On the Testability of Models with Missing Data

Karthika Mohan, Judea Pearl; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:643-650

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Selective Sampling with Drift

Edward Moroshko, Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:651-659

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The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling

Willie Neiswanger, Frank Wood, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:660-668

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Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence

Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus C. Plessis, Frank Chongwoo Park, Daniel D. Lee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:669-677

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Robust Forward Algorithms via PAC-Bayes and Laplace Distributions

Asaf Noy, Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:678-686

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Joint Structure Learning of Multiple Non-Exchangeable Networks

Chris Oates, Sach Mukherjee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:687-695

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Scaling Nonparametric Bayesian Inference via Subsample-Annealing

Fritz Obermeyer, Jonathan Glidden, Eric Jonas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:696-705

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Fast Distribution To Real Regression

Junier Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:706-714

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FuSSO: Functional Shrinkage and Selection Operator

Junier Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:715-723

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To go deep or wide in learning?

Gaurav Pandey, Ambedkar Dukkipati; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:724-732

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LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series

Yubin Park, Carlos Carvalho, Joydeep Ghosh; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:733-742

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Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs

Jon Parker, Hans Engler; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:743-750

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Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming

Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:751-759

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An Efficient Algorithm for Large Scale Compressive Feature Learning

Hristo Paskov, John Mitchell, Trevor Hastie; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:760-768

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Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables

Tomi Peltola, Pasi Jylänki, Aki Vehtari; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:769-777

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An inclusion optimal algorithm for chain graph structure learning

Jose Peña, Dag Sonntag, Jens Nielsen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:778-786

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A Stepwise uncertainty reduction approach to constrained global optimization

Victor Picheny; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:787-795

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Connected Sub-graph Detection

Jing Qian, Venkatesh Saligrama, Yuting Chen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:796-804

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An Analysis of Active Learning with Uniform Feature Noise

Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:805-813

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Black Box Variational Inference

Rajesh Ranganath, Sean Gerrish, David Blei; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:814-822

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Cluster Canonical Correlation Analysis

Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:823-831

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Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process

Vikas Raykar, Priyanka Agrawal; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:832-840

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Learning Structured Models with the AUC Loss and Its Generalizations

Nir Rosenfeld, Ofer Meshi, Danny Tarlow, Amir Globerson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:841-849

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Class Proportion Estimation with Application to Multiclass Anomaly Rejection

Tyler Sanderson, Clayton Scott; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:850-858

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Lifted MAP Inference for Markov Logic Networks

Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:859-867

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Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvarinen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:868-876

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Student-t Processes as Alternatives to Gaussian Processes

Amar Shah, Andrew Wilson, Zoubin Ghahramani; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:877-885

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In Defense of Minhash over Simhash

Anshumali Shrivastava, Ping Li; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:886-894

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Loopy Belief Propagation in the Presence of Determinism

David Smith, Vibhav Gogate; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:895-903

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Explicit Link Between Periodic Covariance Functions and State Space Models

Arno Solin, Simo Särkkä; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:904-912

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Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel

Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:913-921

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SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication

Rebecca Steorts, Rob Hall, Stephen Fienberg; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:922-930

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Adaptive Variable Clustering in Gaussian Graphical Models

Siqi Sun, Yuancheng Zhu, Jinbo Xu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:931-939

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Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch

Partha Talukdar, William Cohen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:940-947

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Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression

Divyanshu Vats, Richard Baraniuk; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:948-957

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Active Learning for Undirected Graphical Model Selection

Divyanshu Vats, Robert Nowak, Richard Baraniuk; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:958-967

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Linear-time training of nonlinear low-dimensional embeddings

Max Vladymyrov, Miguel Carreira-Perpinan; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:968-977

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Gaussian Copula Precision Estimation with Missing Values

Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:978-986

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An LP for Sequential Learning Under Budgets

Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:987-995

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Efficient Algorithms and Error Analysis for the Modified Nystrom Method

Shusen Wang, Zhihua Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:996-1004

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Bayesian Multi-Scale Optimistic Optimization

Ziyu Wang, Babak Shakibi, Lin Jin, Nando Freitas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1005-1014

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Accelerating ABC methods using Gaussian processes

Richard Wilkinson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1015-1023

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A New Approach to Probabilistic Programming Inference

Frank Wood, Jan Willem Meent, Vikash Mansinghka; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1024-1032

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Dynamic Resource Allocation for Optimizing Population Diffusion

Shan Xue, Alan Fern, Daniel Sheldon; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1033-1041

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Mixed Graphical Models via Exponential Families

Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera Allen, Zhandong Liu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1042-1050

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Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies

Juemin Yang, Fang Han, Rafael Irizarry, Han Liu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1051-1059

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Nonparametric estimation and testing of exchangeable graph models

Justin Yang, Christina Han, Edoardo Airoldi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1060-1067

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Generating Efficient MCMC Kernels from Probabilistic Programs

Lingfeng Yang, Patrick Hanrahan, Noah Goodman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1068-1076

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Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

Dani Yogatama, Gideon Mann; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1077-1085

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Accelerated Stochastic Gradient Method for Composite Regularization

Wenliang Zhong, James Kwok; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1086-1094

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Heterogeneous Domain Adaptation for Multiple Classes

Joey Tianyi Zhou, Ivor W.Tsang, Sinno Jialin Pan, Mingkui Tan; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1095-1103

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