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ORCID12. AISTATS 2009: Clearwater Beach, Florida, USA
- David A. Van Dyk, Max Welling:
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, AISTATS 2009, Clearwater Beach, Florida, USA, April 16-18, 2009. JMLR Proceedings 5, JMLR.org 2009 - David A. Van Dyk, Max Welling:
Preface. - Margareta Ackerman, Shai Ben-David:
Clusterability: A Theoretical Study. 1-8 - Mauricio A. Álvarez, David Luengo, Neil D. Lawrence:
Latent Force Models. 9-16 - Artin Armagan:
Variational Bridge Regression. 17-24 - Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková:
Learning Low Density Separators. 25-32 - Liefeng Bo, Cristian Sminchisescu:
Supervised Spectral Latent Variable Models. 33-40 - Héctor Corrada Bravo, Stephen J. Wright, Kevin H. Eng, Sündüz Keles, Grace Wahba:
Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming. 41-48 - Gavin Brown:
A New Perspective for Information Theoretic Feature Selection. 49-56 - Alberto Giovanni Busetto, Joachim M. Buhmann:
Structure Identification by Optimized Interventions. 57-64 - Kevin Robert Canini, Lei Shi, Thomas L. Griffiths:
Online Inference of Topics with Latent Dirichlet Allocation. 65-72 - Carlos M. Carvalho, Nicholas G. Polson, James G. Scott:
Handling Sparsity via the Horseshoe. 73-80 - Jonathan D. Chang, David M. Blei:
Relational Topic Models for Document Networks. 81-88 - Wei Chu, Zoubin Ghahramani:
Probabilistic Models for Incomplete Multi-dimensional Arrays. 89-96 - Stéphan Clémençon, Nicolas Vayatis:
On Partitioning Rules for Bipartite Ranking. 97-104 - Koby Crammer, Mehryar Mohri, Fernando Pereira:
Gaussian Margin Machines. 105-112 - Dafna Shahaf, Carlos Guestrin:
Learning Thin Junction Trees via Graph Cuts. 113-120 - Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor:
Matching Pursuit Kernel Fisher Discriminant Analysis. 121-128 - Joshua V. Dillon, Guy Lebanon:
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood. 129-136 - Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh:
Variational Inference for the Indian Buffet Process. 137-144 - Frederik Eaton, Zoubin Ghahramani:
Choosing a Variable to Clamp. 145-152 - Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent:
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. 153-160 - Inmar E. Givoni, Brendan J. Frey:
Semi-Supervised Affinity Propagation with Instance-Level Constraints. 161-168 - Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert D. Nowak:
Multi-Manifold Semi-Supervised Learning. 169-176 - Joseph Gonzalez, Yucheng Low, Carlos Guestrin:
Residual Splash for Optimally Parallelizing Belief Propagation. 177-184 - Yue Guan, Jennifer G. Dy:
Sparse Probabilistic Principal Component Analysis. 185-192 - Saptarshi Guha, Paul Kidwell, Ryan Hafen, William S. Cleveland:
Visualization Databases for the Analysis of Large Complex Datasets. 193-200 - Andrew Guillory, Erick Chastain, Jeff A. Bilmes:
Active Learning as Non-Convex Optimization. 201-208 - Steve Hanneke, Eric P. Xing:
Network Completion and Survey Sampling. 209-215 - Jarvis D. Haupt, Rui M. Castro, Robert D. Nowak:
Distilled sensing: selective sampling for sparse signal recovery. 216-223 - Katherine A. Heller, Yee Whye Teh, Dilan Görür:
Infinite Hierarchical Hidden Markov Models. 224-231 - Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. 232-239 - Bert Huang, Ansaf Salleb-Aouissi:
Maximum Entropy Density Estimation with Incomplete Presence-Only Data. 240-247 - Jonathan Huang, Carlos Guestrin, Xiaoye Jiang, Leonidas J. Guibas:
Exploiting Probabilistic Independence for Permutations. 248-255 - Alexander Ihler, David A. McAllester:
Particle Belief Propagation. 256-263 - Michael Johanson, Michael H. Bowling:
Data Biased Robust Counter Strategies. 264-271 - Varun Kanade, H. Brendan McMahan, Brent Bryan:
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. 272-279 - Minyoung Kim, Vladimir Pavlovic
:
Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings. 280-287 - Nicole Krämer, Masashi Sugiyama, Mikio L. Braun:
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. 288-295 - Brian Kulis, Suvrit Sra, Inderjit S. Dhillon:
Convex Perturbations for Scalable Semidefinite Programming. 296-303 - Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Sampling Techniques for the Nystrom Method. 304-311 - Hugo Larochelle, Dumitru Erhan, Pascal Vincent:
Deep Learning using Robust Interdependent Codes. 312-319 - Hyekyoung Lee, Seungjin Choi:
Group Nonnegative Matrix Factorization for EEG Classification. 320-327 - Fuxin Li, Yun-Shan Fu, Yu-Hong Dai, Cristian Sminchisescu, Jue Wang:
Kernel Learning by Unconstrained Optimization. 328-335 - Wu-Jun Li, Zhihua Zhang, Dit-Yan Yeung:
Latent Wishart Processes for Relational Kernel Learning. 336-343 - Yufeng Li, Ivor W. Tsang, James Tin-Yau Kwok, Zhi-Hua Zhou:
Tighter and Convex Maximum Margin Clustering. 344-351 - Yuxi Li, Csaba Szepesvári, Dale Schuurmans:
Learning Exercise Policies for American Options. 352-359 - Yuanqing Lin, Shenghuo Zhu, Daniel D. Lee, Ben Taskar:
Learning Sparse Markov Network Structure via Ensemble-of-Trees Models. 360-367 - Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten M. Borgwardt:
A kernel method for unsupervised structured network inference. 368-375 - Han Liu, Jian Zhang:
Estimation Consistency of the Group Lasso and its Applications. 376-383 - Laurens van der Maaten:
Learning a Parametric Embedding by Preserving Local Structure. 384-391 - Bhushan Mandhani, Marina Meila:
Tractable Search for Learning Exponential Models of Rankings. 392-399 - Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum:
Exact and Approximate Sampling by Systematic Stochastic Search. 400-407 - Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
Spanning Tree Approximations for Conditional Random Fields. 408-415 - Liva Ralaivola, Marie Szafranski, Guillaume Stempfel:
Chromatic PAC-Bayes Bounds for Non-IID Data. 416-423 - Nathan D. Ratliff, Brian D. Ziebart, Kevin M. Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, Siddhartha S. Srinivasa:
Inverse Optimal Heuristic Control for Imitation Learning. 424-431 - Steven de Rooij, Tim van Erven:
Learning the Switching Rate by Discretising Bernoulli Sources Online. 432-439 - Dan Roth, Kevin Small, Ivan Titov:
Sequential Learning of Classifiers for Structured Prediction Problems. 440-447 - Ruslan Salakhutdinov, Geoffrey E. Hinton:
Deep Boltzmann Machines. 448-455 - Mark Schmidt, Ewout van den Berg, Michael P. Friedlander, Kevin P. Murphy:
Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. 456-463 - Clayton Scott, Gilles Blanchard:
Novelty detection: Unlabeled data definitely help. 464-471 - Yevgeny Seldin, Naftali Tishby:
PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering. 472-479 - John Shawe-Taylor, David R. Hardoon:
PAC-Bayes Analysis Of Maximum Entropy Classification. 480-487 - Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt:
Efficient graphlet kernels for large graph comparison. 488-495 - Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan:
Hash Kernels. 496-503 - Tomi Silander, Teemu Roos, Petri Myllymäki:
Locally Minimax Optimal Predictive Modeling with Bayesian Networks. 504-511 - Ricardo Bezerra de Andrade e Silva, Robert B. Gramacy:
MCMC Methods for Bayesian Mixtures of Copulas. 512-519 - Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani:
Factorial Mixture of Gaussians and the Marginal Independence Model. 520-527 - Michael Siracusa, John W. Fisher III:
Tractable Bayesian Inference of Time-Series Dependence Structure. 528-535 - Alexander J. Smola, Le Song, Choon Hui Teo:
Relative Novelty Detection. 536-543 - David A. Sontag, Tommi S. Jaakkola:
Tree Block Coordinate Descent for MAP in Graphical Models. 544-551 - Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon, Tai Sing Lee:
The Block Diagonal Infinite Hidden Markov Model. 552-559 - Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol N. Schraudolph:
Variable Metric Stochastic Approximation Theory. 560-566 - Michalis K. Titsias:
Variational Learning of Inducing Variables in Sparse Gaussian Processes. 567-574 - Changhu Wang, Shuicheng Yan, Lei Zhang, HongJiang Zhang:
Non-Negative Semi-Supervised Learning. 575-582 - Chong Wang, Bo Thiesson, Christopher Meek, David M. Blei:
Markov Topic Models. 583-590 - Shijun Wang, Rong Jin:
An Information Geometry Approach for Distance Metric Learning. 591-598 - Zhuoran Wang, John Shawe-Taylor:
Large-Margin Structured Prediction via Linear Programming. 599-606 - Frank D. Wood, Yee Whye Teh:
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. 607-614 - Yongxin Taylor Xi, Zhen James Xiang, Peter J. Ramadge, Robert E. Schapire:
Speed and Sparsity of Regularized Boosting. 615-622 - Yang Xu, Katherine A. Heller, Zoubin Ghahramani:
Tree-Based Inference for Dirichlet Process Mixtures. 623-630 - Min Yang, Yuxi Li, Dale Schuurmans:
Dual Temporal Difference Learning. 631-638 - Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao:
Active Sensing. 639-646 - Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung:
Coherence Functions for Multicategory Margin-based Classification Methods. 647-654 - Zhihua Zhang, Michael I. Jordan:
Latent Variable Models for Dimensionality Reduction. 655-662 - Mingjun Zhong, Mark A. Girolami:
Reversible Jump MCMC for Non-Negative Matrix Factorization. 663-670
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