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ICML 2009 Area Chairs

AC memberDescriptors
Naoki AbeApplications to business analytics and optimization; Web and social network mining; E-commerce; Semi-supervised learning; Clustering; Outlier detection; Cost-sensitive learning; Reinforcement learning; Active learning; Ensemble learning; Online learning; Graphical models
Yasemin Altun Kernel methods; Structured output prediction; Graphical models; Semi-supervised learning; Natural language processing
Francis Bach Kernel methods; Sparse methods; Optimization; Clustering; Semi-supervised learning; Matrix factorization; Computer vision; Learning on graphs
Samy Bengio Applications of speech and image processing; Document retrieval; Large-scale learning; Deep architectures
David Blei Nonparametric Bayesian methods; Topic modeling; Latent variable modeling; Approximate posterior inference; Applications of ML to text
Carla Brodley Applications of ML, including medicine, SNA, science, engineering; Active learning, cost-sensitive learning, and clustering
Florence d'Alche Buc Kernel methods; Structured output prediction; Learning in graphical models; Dynamical systems modeling; Application to computational biology and systems biology
Luc De Raedt Logical and relational learning; Relational learning (statistical); Inductive logic programming (probabilistic); Symbolic and knowledge-based approaches to learning; Pattern mining and inductive querying; Learning from structured data using symbolic methods
Marie desJardins Learning with background knowledge; Active learning; Clustering; Preference learning; Relational learning; Multi-agent learning; Transfer learning
Kurt Driessens Relational reinforcement learning; Transfer learning; Action and activity learning; Relational learning; Inductive logic programming; Multi-agent learning
Alan Fern Reinforcement learning; Relational learning; Structured prediction; Transfer learning; Learning for planning; Learning for search
David Forsyth Computer vision; Object recognition; Computer animation; Human activity recognition
Johannes Fuernkranz Classification-rule learning; Decision-tree learning; Preference learning; Evaluation methodology; ROC analysis; Noise handling; Machine learning in games
Kenji Fukumizu Kernel methods; Dimensionality reduction; Active learning; Dependence analysis; Information geometry
John Langford Learning theory; Interactive learning; Large scale learning; Exploration; Active learning; Reinforcement learning. See also blog
Mirella Lapata Classification and prediction; Data mining; Evaluation and methodology; Information and document retrieval; Natural language processing; Structured and relational data; Web and search
Neil Lawrence Gaussian processes; Dimensionality reduction; latent variable models; Probabilistic models; Approximate inference; Applications in computational biology and human motion
Yann LeCun Deep learning; Vision; Stochastic optimization; Non-convex optimization; Energy-based models; Structured output models; Unsupervised learning; Sparse representations; Models of biological learning; Neural networks
Sofus Attila Macskassy Statistical relational learning; Learning from structured data; Learning on graphs; Pattern and graph mining; Social network analysis; Dynamic network analysis; Semi-supervised learning; ROC analysis; Evaluation methods
Yishay Mansour Computational learning theory; Algorithmic game theory; Theory of Markov decision processes
Steven Minton Learning and the web; Learning to extract information; Learning and planning/scheduling/constraints/problem solving/search; Learning and information integration; Learning methods for record linkage
Dunja MladenicLearning on text/documents; Classification-rule learning; Decision-tree learning; Semi-supervised learning; Feature selection; Personalization and recommendation systems
Tim Oates Reinforcement learning; Machine learning for robotics; Natural language processing; Grammar induction; Computer vision; Grounded language learning
Michael Pazzani Learning and commonsense reasoning; Transfer learning; Personalization and recommendation systems; Empirical insights into ML; Models of Human Learning
Massimiliano Pontil Multi-task learning; Transfer learning; Kernel selection; Multiple kernel learning; Convex optimization; Sparse estimation; Compressed sensing; Matrix factorization; Prediction on graphs; Metric Learning; Clustering; Regularization
Pascal Poupart Reinforcement learning; Bayesian reinforcement learning; Multi-agent reinforcement learning; Inverse reinforcement learning; Predictive state representation; Markov decision processes; Partially observable Markov decision processes; Hidden Markov models; Gaussian processes; Sequential decision making; Active learning; Preference elicitation
Carl Rasmussen Bayesian inference; Gaussian processes; Reinforcement learning; Latent variable models; Approximate inference; Markov chain Monte Carlo
Martin Riedmiller Reinforcement learning; Machine learning for robotics; Policy gradient methods; Neurodynamic programming; Fitted value iteration; Fitted Q iteration; Real life reinforcement learning
Dan Roth On-line classification; Ranking; Structure learning; Learning with constraints; Active learning; Semi-supervised learning; Learning theory; Natural language processing; Information extraction
Volker RothKernel methods; Bayesian inference; Sparsity and feature selection; Clustering; Bio-medical applications & image analysis
Michele Sebag Stochastic optimization; Genetic/evolutionary algorithms; Relational learning; Meta-learning; Clustering; Data streaming; Applications of ML: Autonomic Computing; Robotics
Fei Sha Structured prediction; Manifold learning; Dimensionality reduction; Semi-supervised learning; Optimization; Latent variable modeling; Speech processing and recognition
Yoram Singer Large margin methods; Boosting algorithms; Kernel methods; Structured data; Learning theory
Nathan Srebro Optimization for ML; Multi-task learning; Spectral regularization and matrix factorization approaches; Clustering; Statistical learning theory; Kernel methods; Computational tractability in ML
Luis Torgo Regression; Tree-based models; Prediction of rare values; Utility-based learning; Outlier detection; Time-series analysis; Applications of ML/DM to financial markets; Ecology and fraud detection
Yee Whye Teh Nonparametric Bayesian models; Latent variable models; Graphical models; Probabilistic models; Approximate inference; Deep representations
David Wingate Reinforcement learning; Predictive representations of state; Manifold learning; Bayesian reinforcement learning; Visual perception; Dynamical systems modeling; Hierarchical Bayesian learning
Nevin Zhang Model-based clustering, latent variable models; Learning with probabilistic graphical models
Martin Zinkevich Theory of multi-agents; Mechanism design; Game theory; Online algorithms