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Editors: Yee Whye Teh, Mike Titterington
Preface
; 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
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
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
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
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
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
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
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
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
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
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
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
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
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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