- Preface
- Yee Whye Teh and Mike Titterington
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- Learning the Structure of Deep Sparse Graphical Models
- Ryan Adams, Hanna Wallach, Zoubin Ghahramani ; 9:1-8, 2010.
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- Optimal Allocation Strategies for the Dark Pool Problem
- Alekh Agarwal, Peter Bartlett, Max Dama ; 9:9-16, 2010.
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- Multitask Learning for Brain-Computer Interfaces
- Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun ; 9:17-24, 2010.
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- Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
- Mauricio Álvarez, David Luengo, Michalis Titsias, Neil Lawrence ; 9:25-32, 2010.
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- Learning with Blocks: Composite Likelihood and Contrastive Divergence
- Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth ; 9:33-40, 2010.
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- Deterministic Bayesian inference for the p* model
- Haakon Austad, Nial Friel ; 9:41-48, 2010.
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- Half Transductive Ranking
- Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri ; 9:49-56, 2010.
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- Kernel Partial Least Squares is Universally Consistent
- Gilles Blanchard, Nicole Krämer ; 9:57-64, 2010.
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- Towards Understanding Situated Natural Language
- Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston ; 9:65-72, 2010.
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- Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
- Hei Chan, Manabu Kuroki ; 9:73-80, 2010.
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- Why are DBNs sparse?
- Shaunak Chatterjee, Stuart Russell ; 9:81-88, 2010.
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- Focused Belief Propagation for Query-Specific Inference
- Anton Chechetka, Carlos Guestrin ; 9:89-96, 2010.
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- Parametric Herding
- Yutian Chen, Max Welling ; 9:97-104, 2010.
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- Mass Fatality Incident Identification based on nuclear DNA evidence
- Fabio Corradi ; 9:105-112, 2010.
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- On the Impact of Kernel Approximation on Learning Accuracy
- Corinna Cortes, Mehryar Mohri, Ameet Talwalkar ; 9:113-120, 2010.
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- Improving posterior marginal approximations in latent Gaussian models
- Botond Cseke, Tom Heskes ; 9:121-128, 2010.
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- Impossibility Theorems for Domain Adaptation
- Shai Ben David, Tyler Lu, Teresa Luu, David Pal ; 9:129-136, 2010.
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- Multiclass-Multilabel Classification with More Classes than Examples
- Ofer Dekel, Ohad Shamir ; 9:137-144, 2010.
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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 ; 9:145-152, 2010.
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- Feature Selection using Multiple Streams
- Paramveer Dhillon, Dean Foster, Lyle Ungar ; 9:153-160, 2010.
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- Bayesian variable order Markov models
- Christos Dimitrakakis ; 9:161-168, 2010.
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- Nonparametric Bayesian Matrix Factorization by Power-EP
- Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li ; 9:169-176, 2010.
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- Neural conditional random fields
- Trinh–Minh–Tri Do, Thierry Artieres ; 9:177-184, 2010.
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- Combining Experiments to Discover Linear Cyclic Models with Latent Variables
- Frederick Eberhardt, Patrik Hoyer, Richard Scheines ; 9:185-192, 2010.
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- Graphical Gaussian modelling of multivariate time series with latent variables
- Michael Eichler ; 9:193-200, 2010.
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- Why Does Unsupervised Pre-training Help Deep Learning?
- Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent ; 9:201-208, 2010.
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- Semi-Supervised Learning via Generalized Maximum Entropy
- Ayse Erkan, Yasemin Altun ; 9:209-216, 2010.
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- Model-Free Monte Carlo-like Policy Evaluation
- Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst ; 9:217-224, 2010.
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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 ; 9:225-232, 2010.
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- Posterior distributions are computable from predictive distributions
- Cameron Freer, Daniel Roy ; 9:233-240, 2010.
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- Variational methods for Reinforcement Learning
- Thomas Furmston, David Barber ; 9:241-248, 2010.
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- Understanding the difficulty of training deep feedforward neural networks
- Xavier Glorot, Yoshua Bengio ; 9:249-256, 2010.
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- On Combining Graph-based Variance Reduction schemes
- Vibhav Gogate, Rina Dechter ; 9:257-264, 2010.
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- Locally Linear Denoising on Image Manifolds
- Dian Gong, Fei Sha, Gérard Medioni ; 9:265-272, 2010.
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- Regret Bounds for Gaussian Process Bandit Problems
- Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor ; 9:273-280, 2010.
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- Sufficient covariates and linear propensity analysis
- Hui Guo, Philip Dawid ; 9:281-288, 2010.
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- Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
- Shengbo Guo, Scott Sanner ; 9:289-296, 2010.
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- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
- Michael Gutmann, Aapo Hyvärinen ; 9:297-304, 2010.
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- Boosted Optimization for Network Classification
- Timothy Hancock, Hiroshi Mamitsuka ; 9:305-312, 2010.
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- Dirichlet Process Mixtures of Generalized Linear Models
- Lauren Hannah, David Blei, Warren Powell ; 9:313-320, 2010.
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- Negative Results for Active Learning with Convex Losses
- Steve Hanneke, Liu Yang ; 9:321-325, 2010.
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- Coherent Inference on Optimal Play in Game Trees
- Philipp Hennig, David Stern, Thore Graepel ; 9:326-333, 2010.
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- Collaborative Filtering via Rating Concentration
- Bert Huang, Tony Jebara ; 9:334-341, 2010.
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- Maximum-likelihood learning of cumulative distribution functions on graphs
- Jim Huang, Nebojsa Jojic ; 9:342-349, 2010.
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- Learning Nonlinear Dynamic Models from Non-sequenced Data
- Tzu–Kuo Huang, Le Song, Jeff Schneider ; 9:350-357, 2010.
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- Learning Bayesian Network Structure using LP Relaxations
- Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila ; 9:358-365, 2010.
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- Structured Sparse Principal Component Analysis
- Rodolphe Jenatton, Guillaume Obozinski, Francis Bach ; 9:366-373, 2010.
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- Nonlinear functional regression: a functional RKHS approach
- Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy ; 9:374-380, 2010.
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- Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
- Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari ; 9:381-388, 2010.
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- Collaborative Filtering on a Budget
- Alexandros Karatzoglou, Alex Smola, Markus Weimer ; 9:389-396, 2010.
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- Fast Active-set-type Algorithms for L1-regularized Linear Regression
- Jingu Kim, Haesun Park ; 9:397-404, 2010.
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- Online Anomaly Detection under Adversarial Impact
- Marius Kloft, Pavel Laskov ; 9:405-412, 2010.
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- Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
- Mladen Kolar, Eric Xing ; 9:413-420, 2010.
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- Semi-Supervised Learning with Max-Margin Graph Cuts
- Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang ; 9:421-428, 2010.
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- Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms
- Nevena Lazic, Brendan Frey, Parham Aarabi ; 9:429-436, 2010.
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- Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction
- Guy Lever ; 9:437-444, 2010.
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- The Feature Selection Path in Kernel Methods
- Fuxin Li, Cristian Sminchisescu ; 9:445-452, 2010.
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- Simple Exponential Family PCA
- Jun Li, Dacheng Tao ; 9:453-460, 2010.
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- The Group Dantzig Selector
- Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu ; 9:461-468, 2010.
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- Descent Methods for Tuning Parameter Refinement
- Alexander Lorbert, Peter Ramadge ; 9:469-476, 2010.
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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 ; 9:477-484, 2010.
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- Contextual Multi-Armed Bandits
- Tyler Lu, David Pal, Martin Pal ; 9:485-492, 2010.
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- Exploiting Feature Covariance in High-Dimensional Online Learning
- Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul, Fernando Pereira ; 9:493-500, 2010.
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- Supervised Dimension Reduction Using Bayesian Mixture Modeling
- Kai Mao, Feng Liang, Sayan Mukherjee ; 9:501-508, 2010.
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- Inductive Principles for Restricted Boltzmann Machine Learning
- Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de Freitas ; 9:509-516, 2010.
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- Parallelizable Sampling of Markov Random Fields
- James Martens, Ilya Sutskever ; 9:517-524, 2010.
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- Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
- Julian McAuley, Tiberio Caetano ; 9:525-532, 2010.
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- Discriminative Topic Segmentation of Text and Speech
- Mehryar Mohri, Pedro Moreno, Eugene Weinstein ; 9:533-540, 2010.
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- Elliptical slice sampling
- Iain Murray, Ryan Adams, David MacKay ; 9:541-548, 2010.
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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 ; 9:549-556, 2010.
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- Incremental Sparsification for Real-time Online Model Learning
- Duy Nguyen–Tuong, Jan Peters ; 9:557-564, 2010.
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- Fluid Dynamics Models for Low Rank Discriminant Analysis
- Yung–Kyun Noh, Byoung–Tak Zhang, Daniel Lee ; 9:565-572, 2010.
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- Approximation of hidden Markov models by mixtures of experts with application to particle filtering
- Jimmy Olsson, Jonas Ströjby ; 9:573-580, 2010.
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- A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
- Silvia Pandolfi, Francesco Bartolucci, Nial Friel ; 9:581-588, 2010.
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- Bayesian structure discovery in Bayesian networks with less space
- Pekka Parviainen, Mikko Koivisto ; 9:589-596, 2010.
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- Identifying Cause and Effect on Discrete Data using Additive Noise Models
- Jonas Peters, Dominik Janzing, Bernhard Schölkopf ; 9:597-604, 2010.
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- REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization
- Barnabas Poczos, Sergey Kirshner, Csaba Szepesvári ; 9:605-612, 2010.
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- Infinite Predictor Subspace Models for Multitask Learning
- Piyush Rai, Hal Daume III ; 9:613-620, 2010.
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- Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
- Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey Hinton ; 9:621-628, 2010.
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- Nonparametric prior for adaptive sparsity
- Vikas Raykar, Linda Zhao ; 9:629-636, 2010.
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- Convexity of Proper Composite Binary Losses
- Mark Reid, Robert Williamson ; 9:637-644, 2010.
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- Gaussian processes with monotonicity information
- Jaakko Riihimäki, Aki Vehtari ; 9:645-652, 2010.
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- A Regularization Approach to Nonlinear Variable Selection
- Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa ; 9:653-660, 2010.
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- Efficient Reductions for Imitation Learning
- Stephane Ross, Drew Bagnell ; 9:661-668, 2010.
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- Approximate parameter inference in a stochastic reaction-diffusion model
- Andreas Ruttor, Manfred Opper ; 9:669-676, 2010.
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- Active Sequential Learning with Tactile Feedback
- Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar ; 9:677-684, 2010.
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[supplementary]
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- Reducing Label Complexity by Learning From Bags
- Sivan Sabato, Nathan Srebro, Naftali Tishby ; 9:685-692, 2010.
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- Efficient Learning of Deep Boltzmann Machines
- Ruslan Salakhutdinov, Hugo Larochelle ; 9:693-700, 2010.
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- Factorized Orthogonal Latent Spaces
- Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell ; 9:701-708, 2010.
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- Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
- Mark Schmidt, Kevin Murphy ; 9:709-716, 2010.
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- Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching
- Nic Schraudolph ; 9:717-724, 2010.
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- Dense Message Passing for Sparse Principal Component Analysis
- Kevin Sharp, Magnus Rattray ; 9:725-732, 2010.
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- Empirical Bernstein Boosting
- Pannagadatta Shivaswamy, Tony Jebara ; 9:733-740, 2010.
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- Reduced-Rank Hidden Markov Models
- Sajid Siddiqi, Byron Boots, Geoffrey Gordon ; 9:741-748, 2010.
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- Detecting Weak but Hierarchically-Structured Patterns in Networks
- Aarti Singh, Robert Nowak, Robert Calderbank ; 9:749-756, 2010.
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- Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
- Nikolai Slavov ; 9:757-764, 2010.
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- Nonparametric Tree Graphical Models
- Le Song, Arthur Gretton, Carlos Guestrin ; 9:765-772, 2010.
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[supplementary]
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- On the relation between universality, characteristic kernels and RKHS embedding of measures
- Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet ; 9:773-780, 2010.
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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 ; 9:781-788, 2010.
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- On the Convergence Properties of Contrastive Divergence
- Ilya Sutskever, Tijmen Tieleman ; 9:789-795, 2010.
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- Inference and Learning in Networks of Queues
- Charles Sutton, Michael Jordan ; 9:796-803, 2010.
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- Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
- Taiji Suzuki, Masashi Sugiyama ; 9:804-811, 2010.
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- HOP-MAP: Efficient Message Passing with High Order Potentials
- Daniel Tarlow, Inmar Givoni, Richard Zemel ; 9:812-819, 2010.
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- Hartigan's Method: k-means Clustering without Voronoi
- Matus Telgarsky, Andrea Vattani ; 9:820-827, 2010.
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- Learning Policy Improvements with Path Integrals
- Evangelos Theodorou, Jonas Buchli, Stefan Schaal ; 9:828-835, 2010.
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- Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
- Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth ; 9:836-843, 2010.
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- Bayesian Gaussian Process Latent Variable Model
- Michalis Titsias, Neil Lawrence ; 9:844-851, 2010.
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- A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
- Peter Torma, András György, Csaba Szepesvári ; 9:852-859, 2010.
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- Learning Causal Structure from Overlapping Variable Sets
- Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis ; 9:860-867, 2010.
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- State-Space Inference and Learning with Gaussian Processes
- Ryan Turner, Marc Deisenroth, Carl Rasmussen ; 9:868-875, 2010.
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- Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
- Yener Ulker, Bilge Günsel, Taylan Cemgil ; 9:876-883, 2010.
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- Guarantees for Approximate Incremental SVMs
- Nicolas Usunier, Antoine Bordes, Léon Bottou ; 9:884-891, 2010.
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- An Alternative Prior Process for Nonparametric Bayesian Clustering
- Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller ; 9:892-899, 2010.
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- A Potential-based Framework for Online Multi-class Learning with Partial Feedback
- Shijun Wang, Rong Jin, Hamed Valizadegan ; 9:900-907, 2010.
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- Online Passive-Aggressive Algorithms on a Budget
- Zhuang Wang, Slobodan Vucetic ; 9:908-915, 2010.
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- Structured Prediction Cascades
- David Weiss, Benjamin Taskar ; 9:916-923, 2010.
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- Dependent Indian Buffet Processes
- Sinead Williamson, Peter Orbanz, Zoubin Ghahramani ; 9:924-931, 2010.
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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 ; 9:932-939, 2010.
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- A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra
- Ji Won Yoon, Simon Wilson, K. Hun Mok ; 9:940-947, 2010.
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- Risk Bounds for Levy Processes in the PAC-Learning Framework
- Chao Zhang, Dacheng Tao ; 9:948-955, 2010.
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- Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
- Xinhua Zhang, Thore Graepel, Ralf Herbrich ; 9:956-963, 2010.
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- Multi-Task Learning using Generalized t Process
- Yu Zhang, Dit–Yan Yeung ; 9:964-971, 2010.
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[supplementary]
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- Bayesian Generalized Kernel Models
- Zhihua Zhang, Guang Dai, Donghui Wang, Michael Jordan ; 9:972-979, 2010.
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- Matrix-Variate Dirichlet Process Mixture Models
- Zhihua Zhang, Guang Dai, Michael Jordan ; 9:980-987, 2010.
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- Exclusive Lasso for Multi-task Feature Selection
- Yang Zhou, Rong Jin, Steven Chu–Hong Hoi ; 9:988-995, 2010.
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jmlr.org