Volume 33: Artificial Intelligence and Statistics, 22-25 April 2014, Reykjavik, Iceland

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Editors: Samuel Kaski, Jukka Corander

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Contents:

Preface

Preface

Samuel Kaski, Jukka Corander ; PMLR 33:i-iv

Notable Papers

Decontamination of Mutually Contaminated Models

Gilles Blanchard, Clayton Scott ; PMLR 33:1-9

Distributed optimization of deeply nested systems

Miguel Carreira-Perpinan, Weiran Wang ; PMLR 33:10-19

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

Shinichi Nakajima, Masashi Sugiyama ; PMLR 33:20-28

Regular Papers

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

Nir Ailon ; PMLR 33:29-37

Information-Theoretic Characterization of Sparse Recovery

Cem Aksoylar, Venkatesh Saligrama ; PMLR 33:38-46

Hybrid Discriminative-Generative Approach with Gaussian Processes

Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil Lawrence ; PMLR 33:47-56

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

Waheed Bajwa, Marco Duarte, Robert Calderbank ; PMLR 33:57-67

A New Perspective on Learning Linear Separators with Large L_qL_p Margins

Maria-Florina Balcan, Christopher Berlind ; PMLR 33:68-76

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

Ava Bargi, Richard Yi Xu, Zoubin Ghahramani, Massimo Piccardi ; PMLR 33:77-85

Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability

Jeremias Berg, Matti Järvisalo, Brandon Malone ; PMLR 33:86-95

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

Mathieu Blondel, Yotaro Kubo, Ueda Naonori ; PMLR 33:96-104

PAC-Bayesian Theory for Transductive Learning

Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy ; PMLR 33:105-113

Random Bayesian networks with bounded indegree

Eunice Yuh-Jie Chen, Judea Pearl ; PMLR 33:114-121

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

Jianhui Chen, Tianbao Yang, Shenghuo Zhu ; PMLR 33:122-130

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

Robert Cohn, Satinder Singh, Edmund Durfee ; PMLR 33:131-139

Doubly Aggressive Selective Sampling Algorithms for Classification

Koby Crammer ; PMLR 33:140-148

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 ; PMLR 33:149-158

Sparsity and the truncated l^2-norm

Lee Dicker ; PMLR 33:159-166

Efficient Distributed Topic Modeling with Provable Guarantees

Weicong Ding, Mohammad Rohban, Prakash Ishwar, Venkatesh Saligrama ; PMLR 33:167-175

Pan-sharpening with a Bayesian nonparametric dictionary learning model

Xinghao Ding, Yiyong Jiang, Yue Huang, John Paisley ; PMLR 33:176-184

Approximate Slice Sampling for Bayesian Posterior Inference

Christopher DuBois, Anoop Korattikara, Max Welling, Padhraic Smyth ; PMLR 33:185-193

Bayesian Logistic Gaussian Process Models for Dynamic Networks

Daniele Durante, David Dunson ; PMLR 33:194-201

Avoiding pathologies in very deep networks

David Duvenaud, Oren Rippel, Ryan Adams, Zoubin Ghahramani ; PMLR 33:202-210

Efficient Inference for Complex Queries on Complex Distributions

Lili Dworkin, Michael Kearns, Lirong Xia ; PMLR 33:211-219

Bayesian Switching Interaction Analysis Under Uncertainty

Zoran Dzunic, John Fisher III ; PMLR 33:220-228

Robust learning of inhomogeneous PMMs

Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse ; PMLR 33:229-237

Fully-Automatic Bayesian Piecewise Sparse Linear Models

Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano ; PMLR 33:238-246

Learning with Maximum A-Posteriori Perturbation Models

Andreea Gane, Tamir Hazan, Tommi Jaakkola ; PMLR 33:247-256

Sketching the Support of a Probability Measure

Joachim Giesen, Soeren Laue, Lars Kuehne ; PMLR 33:257-265

Robust Stochastic Principal Component Analysis

John Goes, Teng Zhang, Raman Arora, Gilad Lerman ; PMLR 33:266-274

Bayesian Nonparametric Poisson Factorization for Recommendation Systems

Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David Blei ; PMLR 33:275-283

Efficiently Enforcing Diversity in Multi-Output Structured Prediction

Abner Guzman-Rivera, Pushmeet Kohli, Dhruv Batra, Rob Rutenbar ; PMLR 33:284-292

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 ; PMLR 33:293-302

Analytic Long-Term Forecasting with Periodic Gaussian Processes

Nooshin HajiGhassemi, Marc Deisenroth ; PMLR 33:303-311

On Estimating Causal Effects based on Supplemental Variables

Takahiro Hayashi, Manabu Kuroki ; PMLR 33:312-319

Non-Asymptotic Analysis of Relational Learning with One Network

Peng He, Changshui Zhang ; PMLR 33:320-327

Exploiting the Limits of Structure Learning via Inherent Symmetry

Peng He, Changshui Zhang ; PMLR 33:328-337

A Statistical Model for Event Sequence Data

Kevin Heins, Hal Stern ; PMLR 33:338-346

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Philipp Hennig, Søren Hauberg ; PMLR 33:347-355

Tilted Variational Bayes

James Hensman, Max Zwiessele, Neil Lawrence ; PMLR 33:356-364

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

Matthew Hoffman, Bobak Shahriari, Nando Freitas ; PMLR 33:365-374

Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors

Junya Honda, Akimichi Takemura ; PMLR 33:375-383

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

Jean Honorio, Tommi Jaakkola ; PMLR 33:384-392

Latent Gaussian Models for Topic Modeling

Changwei Hu, Eunsu Ryu, David Carlson, Yingjian Wang, Lawrence Carin ; PMLR 33:393-401

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

Ruitong Huang, Csaba Szepesvari ; PMLR 33:402-410

Global Optimization Methods for Extended Fisher Discriminant Analysis

Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda ; PMLR 33:411-419

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

Rafael Izbicki, Ann Lee, Chad Schafer ; PMLR 33:420-429

Near Optimal Bayesian Active Learning for Decision Making

Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha Srinivasa ; PMLR 33:430-438

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

Shane Jensen, Dean Foster ; PMLR 33:439-447

New Bounds on Compressive Linear Least Squares Regression

Ata Kaban ; PMLR 33:448-456

Recovering Distributions from Gaussian RKHS Embeddings

Motonobu Kanagawa, Kenji Fukumizu ; PMLR 33:457-465

Collaborative Ranking for Local Preferences

Berk Kapicioglu, David Rosenberg, Robert Schapire, Tony Jebara ; PMLR 33:466-474

Scalable Collaborative Bayesian Preference Learning

Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger ; PMLR 33:475-483

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

Do-kyum Kim, Matthew Der, Lawrence Saul ; PMLR 33:484-492

Scalable Variational Bayesian Matrix Factorization with Side Information

Yong-Deok Kim, Seungjin Choi ; PMLR 33:493-502

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

Franz Király, Martin Ehler ; PMLR 33:503-511

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

Jyri Kivinen, Chris Williams, Nicolas Heess ; PMLR 33:512-521

Low-Rank Spectral Learning

Alex Kulesza, N. Raj Rao, Satinder Singh ; PMLR 33:522-530

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

Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric Xing ; PMLR 33:531-539

Computational Education using Latent Structured Prediction

Tanja Käser, Alexander Schwing, Tamir Hazan, Markus Gross ; PMLR 33:540-548

Towards building a Crowd-Sourced Sky Map

Dustin Lang, David Hogg, Bernhard Schölkopf ; PMLR 33:549-557

Incremental Tree-Based Inference with Dependent Normalized Random Measures

Juho Lee, Seungjin Choi ; PMLR 33:558-566

Jointly Informative Feature Selection

Leonidas Lefakis, Francois Fleuret ; PMLR 33:567-575

Learning Heterogeneous Hidden Markov Random Fields

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page ; PMLR 33:576-584

PAC-Bayesian Collective Stability

Ben London, Bert Huang, Ben Taskar, Lise Getoor ; PMLR 33:585-594

Active Area Search via Bayesian Quadrature

Yifei Ma, Roman Garnett, Jeff Schneider ; PMLR 33:595-603

Active Boundary Annotation using Random MAP Perturbations

Subhransu Maji, Tamir Hazan, Tommi Jaakkola ; PMLR 33:604-613

Interpretable Sparse High-Order Boltzmann Machines

Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein ; PMLR 33:614-622

Efficient Lifting of MAP LP Relaxations Using k-Locality

Martin Mladenov, Kristian Kersting, Amir Globerson ; PMLR 33:623-632

A Geometric Algorithm for Scalable Multiple Kernel Learning

John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha ; PMLR 33:633-642

On the Testability of Models with Missing Data

Karthika Mohan, Judea Pearl ; PMLR 33:643-650

Selective Sampling with Drift

Edward Moroshko, Koby Crammer ; PMLR 33:651-659

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

Willie Neiswanger, Frank Wood, Eric Xing ; PMLR 33:660-668

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 ; PMLR 33:669-677

Robust Forward Algorithms via PAC-Bayes and Laplace Distributions

Asaf Noy, Koby Crammer ; PMLR 33:678-686

Joint Structure Learning of Multiple Non-Exchangeable Networks

Chris Oates, Sach Mukherjee ; PMLR 33:687-695

Scaling Nonparametric Bayesian Inference via Subsample-Annealing

Fritz Obermeyer, Jonathan Glidden, Eric Jonas ; PMLR 33:696-705

Fast Distribution To Real Regression

Junier Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing ; PMLR 33:706-714

FuSSO: Functional Shrinkage and Selection Operator

Junier Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng ; PMLR 33:715-723

To go deep or wide in learning?

Gaurav Pandey, Ambedkar Dukkipati ; PMLR 33:724-732

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

Yubin Park, Carlos Carvalho, Joydeep Ghosh ; PMLR 33:733-742

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

Jon Parker, Hans Engler ; PMLR 33:743-750

Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming

Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren ; PMLR 33:751-759

An Efficient Algorithm for Large Scale Compressive Feature Learning

Hristo Paskov, John Mitchell, Trevor Hastie ; PMLR 33:760-768

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

Tomi Peltola, Pasi Jylänki, Aki Vehtari ; PMLR 33:769-777

An inclusion optimal algorithm for chain graph structure learning

Jose Peña, Dag Sonntag, Jens Nielsen ; PMLR 33:778-786

A Stepwise uncertainty reduction approach to constrained global optimization

Victor Picheny ; PMLR 33:787-795

Connected Sub-graph Detection

Jing Qian, Venkatesh Saligrama, Yuting Chen ; PMLR 33:796-804

An Analysis of Active Learning with Uniform Feature Noise

Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman ; PMLR 33:805-813

Black Box Variational Inference

Rajesh Ranganath, Sean Gerrish, David Blei ; PMLR 33:814-822

Cluster Canonical Correlation Analysis

Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal ; PMLR 33:823-831

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

Vikas Raykar, Priyanka Agrawal ; PMLR 33:832-840

Learning Structured Models with the AUC Loss and Its Generalizations

Nir Rosenfeld, Ofer Meshi, Danny Tarlow, Amir Globerson ; PMLR 33:841-849

Class Proportion Estimation with Application to Multiclass Anomaly Rejection

Tyler Sanderson, Clayton Scott ; PMLR 33:850-858

Lifted MAP Inference for Markov Logic Networks

Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate ; PMLR 33:859-867

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

Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvarinen ; PMLR 33:868-876

Student-t Processes as Alternatives to Gaussian Processes

Amar Shah, Andrew Wilson, Zoubin Ghahramani ; PMLR 33:877-885

In Defense of Minhash over Simhash

Anshumali Shrivastava, Ping Li ; PMLR 33:886-894

Loopy Belief Propagation in the Presence of Determinism

David Smith, Vibhav Gogate ; PMLR 33:895-903

Explicit Link Between Periodic Covariance Functions and State Space Models

Arno Solin, Simo Särkkä ; PMLR 33:904-912

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

Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami ; PMLR 33:913-921

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

Rebecca Steorts, Rob Hall, Stephen Fienberg ; PMLR 33:922-930

Adaptive Variable Clustering in Gaussian Graphical Models

Siqi Sun, Yuancheng Zhu, Jinbo Xu ; PMLR 33:931-939

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

Partha Talukdar, William Cohen ; PMLR 33:940-947

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

Divyanshu Vats, Richard Baraniuk ; PMLR 33:948-957

Active Learning for Undirected Graphical Model Selection

Divyanshu Vats, Robert Nowak, Richard Baraniuk ; PMLR 33:958-967

Linear-time training of nonlinear low-dimensional embeddings

Max Vladymyrov, Miguel Carreira-Perpinan ; PMLR 33:968-977

Gaussian Copula Precision Estimation with Missing Values

Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee ; PMLR 33:978-986

An LP for Sequential Learning Under Budgets

Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama ; PMLR 33:987-995

Efficient Algorithms and Error Analysis for the Modified Nystrom Method

Shusen Wang, Zhihua Zhang ; PMLR 33:996-1004

Bayesian Multi-Scale Optimistic Optimization

Ziyu Wang, Babak Shakibi, Lin Jin, Nando Freitas ; PMLR 33:1005-1014

Accelerating ABC methods using Gaussian processes

Richard Wilkinson ; PMLR 33:1015-1023

A New Approach to Probabilistic Programming Inference

Frank Wood, Jan Willem Meent, Vikash Mansinghka ; PMLR 33:1024-1032

Dynamic Resource Allocation for Optimizing Population Diffusion

Shan Xue, Alan Fern, Daniel Sheldon ; PMLR 33:1033-1041

Mixed Graphical Models via Exponential Families

Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera Allen, Zhandong Liu ; PMLR 33:1042-1050

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

Juemin Yang, Fang Han, Rafael Irizarry, Han Liu ; PMLR 33:1051-1059

Nonparametric estimation and testing of exchangeable graph models

Justin Yang, Christina Han, Edoardo Airoldi ; PMLR 33:1060-1067

Generating Efficient MCMC Kernels from Probabilistic Programs

Lingfeng Yang, Patrick Hanrahan, Noah Goodman ; PMLR 33:1068-1076

Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

Dani Yogatama, Gideon Mann ; PMLR 33:1077-1085

Accelerated Stochastic Gradient Method for Composite Regularization

Wenliang Zhong, James Kwok ; PMLR 33:1086-1094

Heterogeneous Domain Adaptation for Multiple Classes

Joey Tianyi Zhou, Ivor W.Tsang, Sinno Jialin Pan, Mingkui Tan ; PMLR 33:1095-1103

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