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