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ICML 2010 - Areas and Chairs

Areas by Chair
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  • Alan Fern (Reinforcement Learning, Inductive Logic Programming and Relational Learning, Planning and Control, Statistical Relational Learning, Structured Output Prediction)
  • Alex Smola (Large-Margin Methods, Kernel Methods, Web Mining)
  • Amos Storkey (Gaussian Processes, Bayesian Inference, Cognitive Models of Learning, Time-Series Analysis)
  • Ashutosh Saxena (Robotics, Planning and Control, Vision)
  • Corinna Cortes (Large-Margin Methods, Kernel Methods, Probabilistic Models, Ranking and Preference Learning)
  • Csaba Szepeszvari (Reinforcement Learning, Learning Theory, Monte Carlo Methods, Online Learning, Planning and Control)
  • Dan Roth (Natural Language Processing, Structured Output Prediction, Text Mining)
  • David Blei (Latent-Variable Models and Topic Models, Bayesian Inference, Monte Carlo Methods, Text Mining)
  • Eamonn Keogh (Time-Series Analysis, Outlier Detection)
  • Eibe Frank (Empirical Insights into ML, Rule and Decision Tree Learning, Supervised Learning)
  • Elena Marchiori (Evolutionary Computation, Bioinformatics, Optimization Algorithms)
  • Eyke Huellermeier (Ranking and Preference Learning, Bioinformatics, Case-Based Reasoning, Fuzzy Learning Systems)
  • Filip Radlinski (Information Retrieval, Ranking and Preference Learning, Web Mining)
  • Francis Bach (Vision, Matrix Factorization Methods, Sparsity and Compressed Sensing, Unsupervised Learning)
  • Hendrik Blockeel (Discovery, Inductive Logic Programming and Relational Learning, Meta-Learning)
  • Henrik Bostroem (Rule and Decision Tree Learning, Inductive Logic Programming and Relational Learning, Supervised Learning)
  • Inderjit Dhillon (Matrix Factorization Methods, Clustering,Graph-Based Learning Methods, Unsupervised Learning)
  • Joao Gama (Data Streams, Constructive Induction and Theory Revision, Online Learning)
  • John Langford (Learning Theory, Active Learning)
  • Jure Leskovec (Social Network Analysis, Graph Mining, Web Mining)
  • Kai Yu (Gaussian Processes, Bayesian Inference, Recommender Systems, Statistical Relational Learning)
  • Katharina Morik (Feature Selection and Dimensionality Reduction,Text Mining, Unsupervised Learning)
  • Katherine Heller (Bayesian Inference, Clustering, Hidden Markov Models, Unsupervised Learning)
  • Kristian Kersting (Statistical Relational Learning, Bayesian Inference, Probabilistic Models)
  • Kurt Driessens (Reinforcement Learning, Game Playing)
  • Leon Bottou (Neural Networks, Deep Architectures, Large-Scale Learning, Optimization Algorithms)
  • Luc De Raedt (Knowledge-Intensive Learning, Inductive Logic Programming and Relational Learning, Pattern Mining and Inductive Querying)
  • Martin Zinkevich (Game Theory, Game Playing, Multi-Agent and Co-Operative Learning, Online Learning)
  • Mikhail Belkin (Manifold Learning, Graph-Based Learning Methods, Matrix Factorization Methods)
  • Mikhail Bilenko (Semi-Supervised Learning, Clustering, Information Retrieval, Unsupervised Learning)
  • Naftali Tishby (Clustering, Bioinformatics, Unsupervised Learning)
  • Nathan Srebro (Matrix Factorization Methods, Large-Scale Learning, Online Learning, Optimization Algorithms)
  • Padraig Cunningham (Case-Based Reasoning, Ensemble Methods, Recommender Systems)
  • Peter Flach (Evaluation Methodology and ROC Analysis, Cost-Sensitive Learning, Inductive Logic Programming and Relational Learning, Supervised Learning)
  • Phil Long (Ensemble Methods, Bioinformatics, Learning Theory, Transfer and Multi-Task Learning)
  • Pierre Dupont (Grammatical Inference, Feature Selection and Dimensionality Reduction, Graph Mining, Hidden Markov Models)
  • Rich Caruana (Transfer and Multi-Task Learning, Deep Architectures, Empirical Insights into ML, Evaluation Methodologyand ROC Analysis, Large-Scale Learning, Neural Networks)
  • Sanjoy Dasgupta (Learning Theory, Active Learning, Clustering, Unsupervised Learning)
  • Sathiya Keerthi (Optimization Algorithms, Large-Margin Methods)
  • Shie Mannor (Reinforcement Learning, Learning Theory, Online Learning, Partially Observable Markov Decision Processes)
  • Thomas Gaertner (Graph Mining, Kernel Methods, Structured Output Prediction, Transfer and Multi-Task Learning)
  • Thomas Hofmann (Structured Output Prediction, Latent-Variable Models and Topic Models, Text Mining, Web Mining)
  • Tobias Scheffer (Active Learning, Semi-Supervised Learning, TextMining, Transfer and Multi-Task Learning)
  • Tong Zhang (Statistical Methods, Graph-Based Learning Methods, Optimization Algorithms)
  • Volker Roth (Feature Selection and Dimensionality Reduction, Bayesian Inference, Clustering, Bioinformatics)
  • Xiaojin Zhu (Cognitive Models of Learning, Latent-Variable Models and Topic Models, Natural Language Processing, Semi-Supervised Learning)


Chairs by Area
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  • Active Learning (John Langford, Sanjoy Dasgupta, Tobias Scheffer)
  • Bayesian Inference (Amos Storkey, David Blei, Kai Yu, Katherine Heller, Kristian Kersting, Volker Roth)
  • Bioinformatics (Elena Marchiori, Eyke Huellermeier, Naftali Tishby, Phil Long, Volker Roth)
  • Case-Based Reasoning (Eyke Huellermeier, Padraig Cunningham)
  • Clustering (Inderjit Dhillon, Katherine Heller, Mikhail Bilenko,Naftali Tishby, Sanjoy Dasgupta, Volker Roth)
  • Cognitive Models of Learning (Amos Storkey, Xiaojin Zhu)
  • Constructive Induction and Theory Revision (Joao Gama)
  • Cost-Sensitive Learning (Peter Flach)
  • Data Streams (Joao Gama)
  • Deep Architectures (Leon Bottou, Rich Caruana)
  • Discovery (Hendrik Blockeel)
  • Empirical Insights into ML (Eibe Frank, Rich Caruana)
  • Ensemble Methods (Padraig Cunningham, Phil Long)
  • Evaluation Methodology and ROC Analysis (Peter Flach, Rich Caruana)
  • Evolutionary Computation (Elena Marchiori)
  • Feature Selection and Dimensionality Reduction (Katharina Morik, Pierre Dupont, Volker Roth)
  • Fuzzy Learning Systems (Eyke Huellermeier)
  • Game Playing (Kurt Driessens, Martin Zinkevich)
  • Game Theory (Martin Zinkevich)
  • Gaussian Processes (Amos Storkey, Kai Yu)
  • Grammatical Inference (Pierre Dupont)
  • Graph Mining (Jure Leskovec, Pierre Dupont, Thomas Gaertner)
  • Graph-Based Learning Methods (Inderjit Dhillon, Mikhail Belkin,Tong Zhang)
  • Hidden Markov Models (Katherine Heller, Pierre Dupont)
  • Inductive Logic Programming and Relational Learning (Hendrik Blockeel, Henrik Bostroem, Luc De Raedt, Peter Flach, Alan Fern)
  • Information Retrieval (Filip Radlinski, Mikhail Bilenko)
  • Kernel Methods (Alex Smola, Corinna Cortes, Thomas Gaertner)
  • Knowledge-Intensive Learning (Luc De Raedt)
  • Large-Margin Methods (Alex Smola, Corinna Cortes, Sathiya Keerthi)
  • Large-Scale Learning (Leon Bottou, Nathan Srebro, Rich Caruana)
  • Latent-Variable Models and Topic Models (David Blei, Thomas Hofmann, Xiaojin Zhu)
  • Learning Theory (John Langford, Phil Long, Sanjoy Dasgupta, Shie Mannor, Csaba Szepeszvari)
  • Manifold Learning (Mikhail Belkin)
  • Matrix Factorization Methods (Francis Bach, Inderjit Dhillon, Mikhail Belkin, Nathan Srebro)
  • Meta-Learning (Hendrik Blockeel)
  • Monte Carlo Methods (David Blei, Csaba Szepeszvari)
  • Multi-Agent and Co-Operative Learning (Martin Zinkevich)
  • Natural Language Processing (Dan Roth, Xiaojin Zhu)
  • Neural Networks (Leon Bottou, Rich Caruana)
  • Online Learning (Joao Gama, Martin Zinkevich, Nathan Srebro, Shie Mannor, Csaba Szepeszvari)
  • Optimization Algorithms (Elena Marchiori, Leon Bottou, Nathan Srebro, Sathiya Keerthi, Tong Zhang)
  • Outlier Detection (Eamonn Keogh)
  • Partially Observable Markov Decision Processes (Shie Mannor)
  • Pattern Mining and Inductive Querying (Luc De Raedt)
  • Planning and Control (Ashutosh Saxena, Csaba Szepeszvari, Alan Fern)
  • Probabilistic Models (Corinna Cortes, Kristian Kersting)
  • Ranking and Preference Learning (Corinna Cortes, Eyke Huellermeier, Filip Radlinski)
  • Recommender Systems (Kai Yu, Padraig Cunningham)
  • Reinforcement Learning (Kurt Driessens, Shie Mannor, Csaba Szepeszvari, Alan Fern)
  • Robotics (Ashutosh Saxena)
  • Rule and Decision Tree Learning (Eibe Frank, Henrik Bostroem)
  • Semi-Supervised Learning (Mikhail Bilenko, Tobias Scheffer, Xiaojin Zhu)
  • Social Network Analysis (Jure Leskovec)
  • Sparsity and Compressed Sensing (Francis Bach)
  • Statistical Methods (Tong Zhang)
  • Statistical Relational Learning (Kai Yu, Kristian Kersting, Alan Fern)
  • Structured Output Prediction (Dan Roth, Thomas Hofmann, Thomas Gaertner, Alan Fern)
  • Supervised Learning (Eibe Frank, Henrik Bostroem, Peter Flach)
  • Text Mining (Dan Roth, David Blei, Katharina Morik, Thomas Hofmann, Tobias Scheffer)
  • Time-Series Analysis (Amos Storkey, Eamonn Keogh)
  • Transfer and Multi-Task Learning (Phil Long, Rich Caruana,Thomas Gaertner, Tobias Scheffer)
  • Unsupervised Learning (Francis Bach, Inderjit Dhillon, Katharina Morik, Katherine Heller, Mikhail Bilenko, Naftali Tishby, Sanjoy Dasgupta)
  • Vision (Ashutosh Saxena, Francis Bach)
  • Web Mining (Alex Smola, Filip Radlinski, Jure Leskovec, Thomas Hofmann)


Invited Application Track Committee
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  • Wolfram Burgard (Robotics)
  • Pedro Felzenszwalb (Vision)
  • Lillian Lee (NLP)
  • Christina Leslie (Computational Biology)
    Jonathan Schaeffer (Computer Games)
  • Yoav Tock (Computer Systems and Networking)
    Gerhard Widmer (Computer Music & Arts)