Preface

Yee Whye Teh, Mike Titterington; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:i-v

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Learning the Structure of Deep Sparse Graphical Models

Ryan P. Adams, Hanna Wallach, Zoubin Ghahramani; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:1-8

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Optimal Allocation Strategies for the Dark Pool Problem

Alekh Agarwal, Peter Bartlett, Max Dama; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:9-16

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Multitask Learning for Brain-Computer Interfaces

Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:17-24

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Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Mauricio Álvarez, David Luengo, Michalis Titsias, Neil D. Lawrence; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:25-32

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Learning with Blocks: Composite Likelihood and Contrastive Divergence

Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:33-40

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Deterministic Bayesian inference for the $p*$ model

Haakon Austad, Nial Friel; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:41-48

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Half Transductive Ranking

Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:49-56

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Kernel Partial Least Squares is Universally Consistent

Gilles Blanchard, Nicole Krämer; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:57-64

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Towards Understanding Situated Natural Language

Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:65-72

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Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs

Hei Chan, Manabu Kuroki; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:73-80

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Why are DBNs sparse?

Shaunak Chatterjee, Stuart Russell; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:81-88

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Focused Belief Propagation for Query-Specific Inference

Anton Chechetka, Carlos Guestrin; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:89-96

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Parametric Herding

Yutian Chen, Max Welling; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:97-104

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Mass Fatality Incident Identification based on nuclear DNA evidence

Fabio Corradi; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:105-112

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On the Impact of Kernel Approximation on Learning Accuracy

Corinna Cortes, Mehryar Mohri, Ameet Talwalkar; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:113-120

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Improving posterior marginal approximations in latent Gaussian models

Botond Cseke, Tom Heskes; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:121-128

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Impossibility Theorems for Domain Adaptation

Shai Ben David, Tyler Lu, Teresa Luu, David Pal; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:129-136

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Multiclass-Multilabel Classification with More Classes than Examples

Ofer Dekel, Ohad Shamir; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:137-144

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Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines

Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:145-152

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Feature Selection using Multiple Streams

Paramveer Dhillon, Dean Foster, Lyle Ungar; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:153-160

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Bayesian variable order Markov models

Christos Dimitrakakis; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:161-168

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Nonparametric Bayesian Matrix Factorization by Power-EP

Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:169-176

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Neural conditional random fields

Trinh–Minh–Tri Do, Thierry Artieres; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:177-184

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Combining Experiments to Discover Linear Cyclic Models with Latent Variables

Frederick Eberhardt, Patrik Hoyer, Richard Scheines; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:185-192

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Graphical Gaussian modelling of multivariate time series with latent variables

Michael Eichler; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:193-200

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Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:201-208

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Semi-Supervised Learning via Generalized Maximum Entropy

Ayse Erkan, Yasemin Altun; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:209-216

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Model-Free Monte Carlo-like Policy Evaluation

Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:217-224

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A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation

Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo, Christian Barillot, Michel Dojat; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:225-232

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Posterior distributions are computable from predictive distributions

Cameron Freer, Daniel Roy; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:233-240

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Variational methods for Reinforcement Learning

Thomas Furmston, David Barber; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:241-248

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Understanding the difficulty of training deep feedforward neural networks

Xavier Glorot, Yoshua Bengio; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256

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On Combining Graph-based Variance Reduction schemes

Vibhav Gogate, Rina Dechter; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:257-264

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Locally Linear Denoising on Image Manifolds

Dian Gong, Fei Sha, Gérard Medioni; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:265-272

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Regret Bounds for Gaussian Process Bandit Problems

Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:273-280

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Sufficient covariates and linear propensity analysis

Hui Guo, Philip Dawid; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:281-288

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Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries

Shengbo Guo, Scott Sanner; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:289-296

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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Michael Gutmann, Aapo Hyvärinen; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:297-304

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Boosted Optimization for Network Classification

Timothy Hancock, Hiroshi Mamitsuka; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:305-312

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Dirichlet Process Mixtures of Generalized Linear Models

Lauren Hannah, David Blei, Warren Powell; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:313-320

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Negative Results for Active Learning with Convex Losses

Steve Hanneke, Liu Yang; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:321-325

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Coherent Inference on Optimal Play in Game Trees

Philipp Hennig, David Stern, Thore Graepel; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:326-333

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Collaborative Filtering via Rating Concentration

Bert Huang, Tony Jebara; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:334-341

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Maximum-likelihood learning of cumulative distribution functions on graphs

Jim Huang, Nebojsa Jojic; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:342-349

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Learning Nonlinear Dynamic Models from Non-sequenced Data

Tzu–Kuo Huang, Le Song, Jeff Schneider; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:350-357

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Learning Bayesian Network Structure using LP Relaxations

Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:358-365

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Structured Sparse Principal Component Analysis

Rodolphe Jenatton, Guillaume Obozinski, Francis Bach; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:366-373

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Nonlinear functional regression: a functional RKHS approach

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:374-380

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Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:381-388

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Collaborative Filtering on a Budget

Alexandros Karatzoglou, Alex Smola, Markus Weimer; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:389-396

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Fast Active-set-type Algorithms for $l1$-regularized Linear Regression

Jingu Kim, Haesun Park; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:397-404

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Online Anomaly Detection under Adversarial Impact

Marius Kloft, Pavel Laskov; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:405-412

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Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach

Mladen Kolar, Eric Xing; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:413-420

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Semi-Supervised Learning with Max-Margin Graph Cuts

Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:421-428

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Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms

Nevena Lazic, Brendan Frey, Parham Aarabi; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:429-436

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Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction

Guy Lever; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:437-444

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The Feature Selection Path in Kernel Methods

Fuxin Li, Cristian Sminchisescu; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:445-452

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Simple Exponential Family PCA

Jun Li, Dacheng Tao; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:453-460

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The Group Dantzig Selector

Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:461-468

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Descent Methods for Tuning Parameter Refinement

Alexander Lorbert, Peter Ramadge; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:469-476

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Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net

Alexander Lorbert, David Eis, Victoria Kostina, David Blei, Peter Ramadge; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:477-484

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Contextual Multi-Armed Bandits

Tyler Lu, David Pal, Martin Pal; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:485-492

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Exploiting Feature Covariance in High-Dimensional Online Learning

Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul, Fernando Pereira; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:493-500

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Supervised Dimension Reduction Using Bayesian Mixture Modeling

Kai Mao, Feng Liang, Sayan Mukherjee; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:501-508

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Inductive Principles for Restricted Boltzmann Machine Learning

Benjamin Marlin, Kevin Swersky, Bo Chen, Nando Freitas; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:509-516

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Parallelizable Sampling of Markov Random Fields

James Martens, Ilya Sutskever; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:517-524

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Exploiting Within-Clique Factorizations in Junction-Tree Algorithms

Julian McAuley, Tiberio Caetano; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:525-532

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Discriminative Topic Segmentation of Text and Speech

Mehryar Mohri, Pedro Moreno, Eugene Weinstein; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:533-540

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Elliptical slice sampling

Iain Murray, Ryan Adams, David MacKay; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:541-548

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Near-Optimal Evasion of Convex-Inducing Classifiers

Blaine Nelson, Benjamin Rubinstein, Ling Huang, Anthony Joseph, Shing–hon Lau, Steven Lee, Satish Rao, Anthony Tran, Doug Tygar; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:549-556

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Incremental Sparsification for Real-time Online Model Learning

Duy Nguyen–Tuong, Jan Peters; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:557-564

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Fluid Dynamics Models for Low Rank Discriminant Analysis

Yung–Kyun Noh, Byoung–Tak Zhang, Daniel Lee; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:565-572

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Approximation of hidden Markov models by mixtures of experts with application to particle filtering

Jimmy Olsson, Jonas Ströjby; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:573-580

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A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection

Silvia Pandolfi, Francesco Bartolucci, Nial Friel; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:581-588

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Bayesian structure discovery in Bayesian networks with less space

Pekka Parviainen, Mikko Koivisto; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:589-596

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Identifying Cause and Effect on Discrete Data using Additive Noise Models

Jonas Peters, Dominik Janzing, Bernhard Schölkopf; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:597-604

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REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization

Barnabas Poczos, Sergey Kirshner, Csaba Szepesvári; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:605-612

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Infinite Predictor Subspace Models for Multitask Learning

Piyush Rai, Hal Daumé III; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:613-620

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Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Marc’Aurelio Ranzato, Alex Krizhevsky, Geoffrey Hinton; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:621-628

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Nonparametric prior for adaptive sparsity

Vikas Raykar, Linda Zhao; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:629-636

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Convexity of Proper Composite Binary Losses

Mark Reid, Robert Williamson; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:637-644

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Gaussian processes with monotonicity information

Jaakko Riihimäki, Aki Vehtari; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:645-652

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A Regularization Approach to Nonlinear Variable Selection

Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:653-660

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Efficient Reductions for Imitation Learning

Stephane Ross, Drew Bagnell; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:661-668

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Approximate parameter inference in a stochastic reaction-diffusion model

Andreas Ruttor, Manfred Opper; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:669-676

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Active Sequential Learning with Tactile Feedback

Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:677-684

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Reducing Label Complexity by Learning From Bags

Sivan Sabato, Nathan Srebro, Naftali Tishby; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:685-692

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Efficient Learning of Deep Boltzmann Machines

Ruslan Salakhutdinov, Hugo Larochelle; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:693-700

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Factorized Orthogonal Latent Spaces

Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:701-708

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Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

Mark Schmidt, Kevin Murphy; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:709-716

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Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching

Nic Schraudolph; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:717-724

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Dense Message Passing for Sparse Principal Component Analysis

Kevin Sharp, Magnus Rattray; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:725-732

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Empirical Bernstein Boosting

Pannagadatta Shivaswamy, Tony Jebara; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:733-740

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Reduced-Rank Hidden Markov Models

Sajid Siddiqi, Byron Boots, Geoffrey Gordon; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:741-748

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Detecting Weak but Hierarchically-Structured Patterns in Networks

Aarti Singh, Robert Nowak, Robert Calderbank; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:749-756

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Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

Nikolai Slavov; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:757-764

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Nonparametric Tree Graphical Models

Le Song, Arthur Gretton, Carlos Guestrin; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:765-772

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On the relation between universality, characteristic kernels and RKHS embedding of measures

Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:773-780

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Conditional Density Estimation via Least-Squares Density Ratio Estimation

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:781-788

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On the Convergence Properties of Contrastive Divergence

Ilya Sutskever, Tijmen Tieleman; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:789-795

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Inference and Learning in Networks of Queues

Charles Sutton, Michael I. Jordan; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:796-803

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Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation

Taiji Suzuki, Masashi Sugiyama; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:804-811

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HOP-MAP: Efficient Message Passing with High Order Potentials

Daniel Tarlow, Inmar Givoni, Richard Zemel; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:812-819

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Hartigan’s Method: k-means Clustering without Voronoi

Matus Telgarsky, Andrea Vattani; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:820-827

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Learning Policy Improvements with Path Integrals

Evangelos Theodorou, Jonas Buchli, Stefan Schaal; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:828-835

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Unsupervised Aggregation for Classification Problems with Large Numbers of Categories

Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:836-843

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Bayesian Gaussian Process Latent Variable Model

Michalis Titsias, Neil D. Lawrence; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:844-851

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A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping

Peter Torma, András György, Csaba Szepesvári; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:852-859

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Learning Causal Structure from Overlapping Variable Sets

Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:860-867

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State-Space Inference and Learning with Gaussian Processes

Ryan Turner, Marc Deisenroth, Carl Rasmussen; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:868-875

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Sequential Monte Carlo Samplers for Dirichlet Process Mixtures

Yener Ulker, Bilge Günsel, Taylan Cemgil; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:876-883

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Guarantees for Approximate Incremental SVMs

Nicolas Usunier, Antoine Bordes, Léon Bottou; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:884-891

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An Alternative Prior Process for Nonparametric Bayesian Clustering

Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:892-899

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A Potential-based Framework for Online Multi-class Learning with Partial Feedback

Shijun Wang, Rong Jin, Hamed Valizadegan; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:900-907

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Online Passive-Aggressive Algorithms on a Budget

Zhuang Wang, Slobodan Vucetic; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:908-915

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Structured Prediction Cascades

David Weiss, Benjamin Taskar; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:916-923

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Dependent Indian Buffet Processes

Sinead Williamson, Peter Orbanz, Zoubin Ghahramani; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:924-931

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Modeling annotator expertise: Learning when everybody knows a bit of something

Yan Yan, Romer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo, Luca Bogoni, Linda Moy, Jennifer Dy; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:932-939

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A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon, Simon Wilson, K. Hun Mok; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:940-947

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Risk Bounds for Levy Processes in the PAC-Learning Framework

Chao Zhang, Dacheng Tao; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:948-955

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Bayesian Online Learning for Multi-label and Multi-variate Performance Measures

Xinhua Zhang, Thore Graepel, Ralf Herbrich; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:956-963

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Multi-Task Learning using Generalized t Process

Yu Zhang, Dit–Yan Yeung; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:964-971

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Bayesian Generalized Kernel Models

Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:972-979

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Matrix-Variate Dirichlet Process Mixture Models

Zhihua Zhang, Guang Dai, Michael I. Jordan; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:980-987

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Exclusive Lasso for Multi-task Feature Selection

Yang Zhou, Rong Jin, Steven Chu–Hong Hoi; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:988-995

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