Edoardo Airoldi, Harvard University; David Blei, Princeton University; Jake Hofman, Yahoo!, Inc.; Tony Jebara, Columbia University; Eric Xing, Carnegie Mellon University

Approximate inference - how far have we come?

Amir Globerson, Hebrew University; David Sontag, New York University; Tommi Jaakkola, Massachusetts Institute of Technology

Beyond Search: Computational Intelligence for the Web (day 1)

Anton Schwaighofer, Microsoft Research Cambridge (UK); Junfeng Pan, Google; Thomas Borchert, Microsoft Research Cambridge (UK); Olivier Chapelle, Yahoo! Research; Joaquin Quiñonero Candela, Microsoft adCenter

Beyond Search: Computational Intelligence for the Web (day 2)

Causality: objectives and assessment

Isabelle Guyon, Clopinet; Dominik Janzing, Universität Karlsruhe; Bernhard Schölkopf, Max Planck Institute for Intelligent Systems

Cortical Microcircuits and their Computational Functions

Tomaso Poggio, McGovern Institute for Brain Research at MIT; Terrence Sejnowski, Salk Institute

Cost Sensitive Learning

Balaji Krishnapuram, Siemens Medical Solutions USA, Inc.; Shipeng Yu, University of Munich; Oksana Yakhnenko, Iowa State University; R. Bharat Rao, ; Lawrence Carin, Duke University

Kernel Learning: Automatic Selection of Optimal Kernels

Corinna Cortes, Google, Inc; Arthur Gretton, CMU and MPI; Gert Lanckriet, U.C. San Diego; Mehryar Mohri, Google Research; Afshin Rostamizadeh, Courant Institute, New York University

Learning from Multiple Sources

David Hardoon, SAS; Gayle Leen, Helsinki University of Technology; Samuel Kaski, Aalto University and University of Helsinki; John Shawe-Taylor, University College London

Machine Learning in Computational Biology

Gal Chechik, Google, BIU; Christina Leslie, Memorial Sloan-Kettering Cancer Center; Quaid Morris, University of Toronto; William Noble, University of Washington; Gunnar Raetsch, Max Planck Society

Machine learning meets human learning

Nathaniel Daw, New York University; Tom Griffiths, University of California, Berkeley; Josh Tenenbaum, Massachusetts Institute of Technology; Xiaojin (Jerry) Zhu, U. Wisconsin-Madison

Machine Learning Open Source Software

Soeren Sonnenburg, TomTom; Mikio Braun, TU Berlin; Cheng Soon Ong, ETH Zurich

Model Uncertainty and Risk in Reinforcement Learning

Yaakov Engel, Rafael; Mohammad Ghavamzadeh, INRIA Lille - Nord Europe; Shie Mannor, McGill University; Pascal Poupart, University of Waterloo

New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces

Maria Florina Balcan, Georgia Institute of Technology; Shai Ben-David, University of Waterloo; Avrim Blum, Carnegie Mellon University; Kristiaan Pelckmans, K.U.Leuven; John Shawe-Taylor, University College London

New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis

Melissa Carroll, Princeton University; Irina Rish, IBM T.J. Watson Research Center; Francisco Pereira, Princeton University; Guillermo Cecchi, IBM Research

Optimization for Machine Learning

Suvrit Sra, Max Planck Institute for Intelligent Systems; Sebastian Nowozin, Microsoft Research Cambridge; Vishwanathan S V N, National ICT Australia

Parallel Implementations of Learning Algorithms: What have you done for me lately?

Robert Thibadeau, Seagate Technology; Dan Hammerstrom, Portland State University; David Touretzky, Carnegie Mellon University; Tom Mitchell, Carnegie Mellon University

Principled Theoretical Frameworks for the Perception-Action Cycle

Daniel Polani, University of Hertfordshire; Naftali Tishby, The Hebrew University Jerusalem

Probabilistic Programming: Universal Languages, Systems and Applications

Dan Roy, University of Cambridge; John Winn, Microsoft Research Cambridge; David McAllester, TTI-Chicago; Vikash Mansinghka, MIT and Prior Knowledge, Inc; Josh Tenenbaum, Massachusetts Institute of Technology

Speech and Language: Learning-based Methods and Systems

Xiaodong He, Microsoft Research; Li Deng, Microsoft Corporation

Speech and Language: Unsupervised Latent-Variable Models

Slav Petrov, Google; Aria Haghighi, Unversity of California, Berkeley; Percy Liang, Stanford University; Dan Klein, UC Berkeley

Statistical Analysis and Modeling of Response Dependencies in Neural Populations (day 1)

Klaus Obermayer, Technical University of Berlin; Valentin Dragoi, University of Texas-Houston Medical School; Arno Onken, Université de Genève; Steffen Grünewälder, Technische Universität Berlin; Denise Berger, Freie Universitaet Berlin

Statistical Analysis and Modeling of Response Dependencies in Neural Populations (day 2)

Stochastic Models of Behaviour

Aldo Faisal, Imperial College London; Marta Gonzalez, Northeastern University

Structured Input - Structured Output

Karsten Borgwardt, MPIs Tuebingen; Koji Tsuda, AIST; Vishwanathan S V N, National ICT Australia; Xifeng Yan, IBM T. J. Watson Research Center