### ICML 2009 Area Chairs

AC member | Descriptors |

Naoki Abe | Applications 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 Mladenic | Learning 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 Roth | Kernel 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 |