Volume 9, March 2010

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  Yee Whye Teh, D. Mike Titterington: Preface.
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  Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani: Learning the Structure of Deep Sparse Graphical Models. 1-8
• 
  Alekh Agarwal, Peter L. Bartlett, Max Dama: Optimal Allocation Strategies for the Dark Pool Problem. 9-16
• 
  Morteza Alamgir, Moritz Grosse-Wentrup, Yasemin Altun: Multitask Learning for Brain-Computer Interfaces. 17-24
• 
  Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence: Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. 25-32
• 
  Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth: Learning with Blocks: Composite Likelihood and Contrastive Divergence. 33-40
• 
  Haakon Austad, Nial Friel: Deterministic Bayesian inference for the p* model. 41-48
• 
  Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri: Half Transductive Ranking. 49-56
• 
  Gilles Blanchard, Nicole Krämer: Kernel Partial Least Squares is Universally Consistent. 57-64
• 
  Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston: Towards Understanding Situated Natural Language. 65-72
• 
  Hei Chan, Manabu Kuroki: Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs. 73-80
• 
  Shaunak Chatterjee, Stuart Russell: Why are DBNs sparse? 81-88
• 
  Anton Chechetka, Carlos Guestrin: Focused Belief Propagation for Query-Specific Inference. 89-96
• 
  Yutian Chen, Max Welling: Parametric Herding. 97-104
• 
  Fabio Corradi: Mass Fatality Incident Identification based on nuclear DNA evidence. 105-112
• 
  Corinna Cortes, Mehryar Mohri, Ameet Talwalkar: On the Impact of Kernel Approximation on Learning Accuracy. 113-120
• 
  Botond Cseke, Tom Heskes: Improving posterior marginal approximations in latent Gaussian models. 121-128
• 
  Shai Ben-David, Tyler Lu, Teresa Luu, Dávid Pál: Impossibility Theorems for Domain Adaptation. 129-136
• 
  Ofer Dekel, Ohad Shamir: Multiclass-Multilabel Classification with More Classes than Examples. 137-144
• 
  Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau: Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. 145-152
• 
  Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar: Feature Selection using Multiple Streams. 153-160
• 
  Christos Dimitrakakis: Bayesian variable order Markov models. 161-168
• 
  Nan Ding, Yuan (Alan) Qi, Rongjing Xiang, Ian Molloy, Ninghui Li: Nonparametric Bayesian Matrix Factorization by Power-EP. 169-176
• 
  Trinh Minh Tri Do, Thierry Artières: Neural conditional random fields. 177-184
• 
  Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines: Combining Experiments to Discover Linear Cyclic Models with Latent Variables. 185-192
• 
  Michael Eichler: Graphical Gaussian modelling of multivariate time series with latent variables. 193-200
• 
  Dumitru Erhan, Aaron C. Courville, Yoshua Bengio, Pascal Vincent: Why Does Unsupervised Pre-training Help Deep Learning? 201-208
• 
  Ayse Erkan, Yasemin Altun: Semi-Supervised Learning via Generalized Maximum Entropy. 209-216
• 
  Raphael Fonteneau, Susan A. Murphy, Louis Wehenkel, Damien Ernst: Model-Free Monte Carlo-like Policy Evaluation. 217-224
• 
  Florence Forbes, Senan Doyle, Daniel García-Lorenzo, Christian Barillot, Michel Dojat: A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation. 225-232
• 
  Cameron E. Freer, Daniel M. Roy: Posterior distributions are computable from predictive distributions. 233-240
• 
  Thomas Furmston, David Barber: Variational methods for Reinforcement Learning. 241-248
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  Xavier Glorot, Yoshua Bengio: Understanding the difficulty of training deep feedforward neural networks. 249-256
• 
  Vibhav Gogate, Rina Dechter: On Combining Graph-based Variance Reduction schemes. 257-264
• 
  Dian Gong, Fei Sha, Gérard G. Medioni: Locally Linear Denoising on Image Manifolds. 265-272
• 
  Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor: Regret Bounds for Gaussian Process Bandit Problems. 273-280
• 
  Hui Guo, A. Philip Dawid: Sufficient covariates and linear propensity analysis. 281-288
• 
  Shengbo Guo, Scott Sanner: Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. 289-296
• 
  Michael Gutmann, Aapo Hyvärinen: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. 297-304
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  Timothy Hancock, Hiroshi Mamitsuka: Boosted Optimization for Network Classification. 305-312
• 
  Lauren Hannah, David M. Blei, Warren B. Powell: Dirichlet Process Mixtures of Generalized Linear Models. 313-320
• 
  Steve Hanneke, Liu Yang: Negative Results for Active Learning with Convex Losses. 321-325
• 
  Philipp Hennig, David H. Stern, Thore Graepel: Coherent Inference on Optimal Play in Game Trees. 326-333
• 
  Bert C. Huang, Tony Jebara: Collaborative Filtering via Rating Concentration. 334-341
• 
  Jim C. Huang, Nebojsa Jojic: Maximum-likelihood learning of cumulative distribution functions on graphs. 342-349
• 
  Tzu-Kuo Huang, Le Song, Jeff G. Schneider: Learning Nonlinear Dynamic Models from Non-sequenced Data. 350-357
• 
  Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila: Learning Bayesian Network Structure using LP Relaxations. 358-365
• 
  Rodolphe Jenatton, Guillaume Obozinski, Francis Bach: Structured Sparse Principal Component Analysis. 366-373
• 
  Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy: Nonlinear functional regression: a functional RKHS approach. 374-380
• 
  Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari: Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. 381-388
• 
  Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer: Collaborative Filtering on a Budget. 389-396
• 
  Jingu Kim, Haesun Park: Fast Active-set-type Algorithms for L1-regularized Linear Regression. 397-404
• 
  Marius Kloft, Pavel Laskov: Online Anomaly Detection under Adversarial Impact. 405-412
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  Mladen Kolar, Eric P. Xing: Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach. 413-420
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  Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang: Semi-Supervised Learning with Max-Margin Graph Cuts. 421-428
• 
  Nevena Lazic, Brendan J. Frey, Parham Aarabi: Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms. 429-436
• 
  Guy Lever: Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction. 437-444
• 
  Fuxin Li, Cristian Sminchisescu: The Feature Selection Path in Kernel Methods. 445-452
• 
  Jun Li, Dacheng Tao: Simple Exponential Family PCA. 453-460
• 
  Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu: The Group Dantzig Selector. 461-468
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  Alexander Lorbert, Peter J. Ramadge: Descent Methods for Tuning Parameter Refinement. 469-476
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  Alexander Lorbert, David Eis, Victoria Kostina, David M. Blei, Peter J. Ramadge: Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net. 477-484
• 
  Tyler Lu, Dávid Pál, Martin Pal: Contextual Multi-Armed Bandits. 485-492
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  Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira: Exploiting Feature Covariance in High-Dimensional Online Learning. 493-500
• 
  Kai Mao, Feng Liang, Sayan Mukherjee: Supervised Dimension Reduction Using Bayesian Mixture Modeling. 501-508
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  Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas: Inductive Principles for Restricted Boltzmann Machine Learning. 509-516
• 
  James Martens, Ilya Sutskever: Parallelizable Sampling of Markov Random Fields. 517-524
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  Julian John McAuley, Tibério S. Caetano: Exploiting Within-Clique Factorizations in Junction-Tree Algorithms. 525-532
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  Mehryar Mohri, Pedro Moreno, Eugene Weinstein: Discriminative Topic Segmentation of Text and Speech. 533-540
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  Iain Murray, Ryan Prescott Adams, David J. C. MacKay: Elliptical slice sampling. 541-548
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  Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. Doug Tygar: Near-Optimal Evasion of Convex-Inducing Classifiers. 549-556
• 
  Duy Nguyen-Tuong, Jan Peters: Incremental Sparsification for Real-time Online Model Learning. 557-564
• 
  Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee: Fluid Dynamics Models for Low Rank Discriminant Analysis. 565-572
• 
  Jimmy Olsson, Jonas Ströjby: Approximation of hidden Markov models by mixtures of experts with application to particle filtering. 573-580
• 
  Silvia Pandolfi, Francesco Bartolucci, Nial Friel: A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection. 581-588
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  Pekka Parviainen, Mikko Koivisto: Bayesian structure discovery in Bayesian networks with less space. 589-596
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  Jonas Peters, Dominik Janzing, Bernhard Schölkopf: Identifying Cause and Effect on Discrete Data using Additive Noise Models. 597-604
• 
  Barnabás Póczos, Sergey Kirshner, Csaba Szepesvári: REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization. 605-612
• 
  Piyush Rai, Hal Daumé III: Infinite Predictor Subspace Models for Multitask Learning. 613-620
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  Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton: Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images. 621-628
• 
  Vikas C. Raykar, Linda H. Zhao: Nonparametric prior for adaptive sparsity. 629-636
• 
  Mark D. Reid, Robert C. Williamson: Convexity of Proper Composite Binary Losses. 637-644
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  Jaakko Riihimäki, Aki Vehtari: Gaussian processes with monotonicity information. 645-652
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  Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa: A Regularization Approach to Nonlinear Variable Selection. 653-660
• 
  Stéphane Ross, Drew Bagnell: Efficient Reductions for Imitation Learning. 661-668
• 
  Andreas Ruttor, Manfred Opper: Approximate parameter inference in a stochastic reaction-diffusion model. 669-676
• 
  Hannes P. Saal, Jo-Anne Ting, Sethu Vijayakumar: Active Sequential Learning with Tactile Feedback. 677-684
• 
  Sivan Sabato, Nathan Srebro, Naftali Tishby: Reducing Label Complexity by Learning From Bags. 685-692
• 
  Ruslan Salakhutdinov, Hugo Larochelle: Efficient Learning of Deep Boltzmann Machines. 693-700
• 
  Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell: Factorized Orthogonal Latent Spaces. 701-708
• 
  Mark W. Schmidt, Kevin P. Murphy: Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials. 709-716
• 
  Nic Schraudolph: Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching. 717-724
• 
  Kevin Sharp, Magnus Rattray: Dense Message Passing for Sparse Principal Component Analysis. 725-732
• 
  Pannagadatta K. Shivaswamy, Tony Jebara: Empirical Bernstein Boosting. 733-740
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  Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon: Reduced-Rank Hidden Markov Models. 741-748
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  Aarti Singh, Robert D. Nowak, A. Robert Calderbank: Detecting Weak but Hierarchically-Structured Patterns in Networks. 749-756
• 
  Nikolai Slavov: Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks. 757-764
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  Le Song, Arthur Gretton, Carlos Guestrin: Nonparametric Tree Graphical Models. 765-772
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  Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet: On the relation between universality, characteristic kernels and RKHS embedding of measures. 773-780
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  Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara: Conditional Density Estimation via Least-Squares Density Ratio Estimation. 781-788
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  Ilya Sutskever, Tijmen Tieleman: On the Convergence Properties of Contrastive Divergence. 789-795
• 
  Charles A. Sutton, Michael I. Jordan: Inference and Learning in Networks of Queues. 796-803
• 
  Taiji Suzuki, Masashi Sugiyama: Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation. 804-811
• 
  Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel: HOP-MAP: Efficient Message Passing with High Order Potentials. 812-819
• 
  Matus Telgarsky, Andrea Vattani: Hartigan's Method: k-means Clustering without Voronoi. 820-827
• 
  Evangelos Theodorou, Jonas Buchli, Stefan Schaal: Learning Policy Improvements with Path Integrals. 828-835
• 
  Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth: Unsupervised Aggregation for Classification Problems with Large Numbers of Categories. 836-843
• 
  Michalis K. Titsias, Neil D. Lawrence: Bayesian Gaussian Process Latent Variable Model. 844-851
• 
  Péter Torma, András György, Csaba Szepesvári: A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. 852-859
• 
  Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G. Tollis: Learning Causal Structure from Overlapping Variable Sets. 860-867
• 
  Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen: State-Space Inference and Learning with Gaussian Processes. 868-875
• 
  Yener Ülker, Bilge Günsel, Ali Taylan Cemgil: Sequential Monte Carlo Samplers for Dirichlet Process Mixtures. 876-883
• 
  Nicolas Usunier, Antoine Bordes, Léon Bottou: Guarantees for Approximate Incremental SVMs. 884-891
• 
  Hanna M. Wallach, Shane Jensen, Lee Dicker, Katherine A. Heller: An Alternative Prior Process for Nonparametric Bayesian Clustering. 892-899
• 
  Shijun Wang, Rong Jin, Hamed Valizadegan: A Potential-based Framework for Online Multi-class Learning with Partial Feedback. 900-907
• 
  Zhuang Wang, Slobodan Vucetic: Online Passive-Aggressive Algorithms on a Budget. 908-915
• 
  David Weiss, Benjamin Taskar: Structured Prediction Cascades. 916-923
• 
  Sinead Williamson, Peter Orbanz, Zoubin Ghahramani: Dependent Indian Buffet Processes. 924-931
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  Yan Yan, Rómer Rosales, Glenn Fung, Mark W. Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy: Modeling annotator expertise: Learning when everybody knows a bit of something. 932-939
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  Ji Won Yoon, Simon P. Wilson, K. Hun Mok: A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra. 940-947
• 
  Chao Zhang, Dacheng Tao: Risk Bounds for Levy Processes in the PAC-Learning Framework. 948-955
• 
  Xinhua Zhang, Thore Graepel, Ralf Herbrich: Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. 956-963
• 
  Yu Zhang, Dit-Yan Yeung: Multi-Task Learning using Generalized t Process. 964-971
• 
  Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan: Bayesian Generalized Kernel Models. 972-979
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  Zhihua Zhang, Guang Dai, Michael I. Jordan: Matrix-Variate Dirichlet Process Mixture Models. 980-987
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  Yang Zhou, Rong Jin, Steven C. H. Hoi: Exclusive Lasso for Multi-task Feature Selection. 988-995

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