Barbara Hammer, University of Bielefeld; Laurens van der Maaten, UC San Diego / Delft University of Technology; Fei Sha, University of Southern California; Alexander Smola, Yahoo! Research

Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning

Pierre Baldi, University of California, Irvine; Klaus-Robert Müller, TU Berlin; Gisbert Schneider, ETH

Coarse-to-Fine Learning and Inference

Ben Taskar, University of Pennsylvania; David Weiss, University of Pennsylvania; Benjamin Sapp, University of Pennsylvania; Slav Petrov, Google

Computational Social Science and the Wisdom of Crowds

Jennifer Wortman Vaughan, UCLA; Hanna Wallach, University of Massachusetts Amherst

Decision Making with Multiple Imperfect Decision Makers

Miroslav Karny, Institute of Information Theory and Automation; Tatiana Guy, Institute of Information Theory and Automation; David Wolpert, Los Alamos

Deep Learning and Unsupervised Feature Learning

Honglak Lee, University of Michigan; Marc'Aurelio Ranzato, Google; Yoshua Bengio, University of Montreal; Geoffrey Hinton, University of Toronto; Yann LeCun, New York U; Andrew Ng, Stanford University

Discrete Optimization in Machine Learning: Structures, Algorithms and Applications

Andreas Krause, ETH Zurich; Pradeep Ravikumar, University of Texas, Austin; Jeff Bilmes, University of Washington; Stefanie Jegelka, Max Planck Institute for Intelligent Systems

Learning and Planning from Batch Time Series Data

Dan Lizotte, University of Waterloo; Michael Bowling, University of Alberta; Susan Murphy, University of Michigan; Joelle Pineau, McGill University; Sandeep Vijan, University of Michigan

Learning on Cores, Clusters, and Clouds

Alekh Agarwal, University of California Berkeley; Lawrence Cayton, Max Planck Institute for Biological Cybernetics; Ofer Dekel, Microsoft Research; John Duchi, University of California Berkeley; John Langford, Yahoo

Low-rank Methods for Large-scale Machine Learning

Arthur Gretton, Gatsby Unit, UCL; Michael Mahoney, Stanford; Mehryar Mohri, Google Research; Ameet Talwalkar, UC Berkeley

Machine Learning for Assistive Technologies

Jesse Hoey, University of Waterloo; Pascal Poupart, University of Waterloo; Thomas Ploetz, Newcastle University

Machine Learning for Social Computing

Zenglin Xu, Purdue University; Irwin King, The Chinese University of Hong Kong; Shenghuo Zhu, NEC Laboratories America; Alan Qi, Purdue university; Rong Yan, Facebook; John Yen, Penn State University

Machine Learning in Computational Biology

Gunnar Raetsch, Max Planck Society; Jean-Philippe Vert, ParisTech; Tomer Hertz, Fred Hutchnison Cancer Research Center; Yanjun Qi, NEC Labs America

Machine Learning in Online Advertising

James Shanahan, Independent Consultant; Deepak Agarwal, Yahoo! Research Labs; Tao Qin, Microsoft Reserach Asia; Tie-Yan Liu, Microsoft Research Asia

Machine Learning meets Computational Photography

Stefan Harmeling, Max Planck Institute for Intelligent Systems; Michael Hirsch, UCL; Bill Freeman, Massachusetts Institute of Technology; Peyman Milanfar, University of California

Modeling Human Communication Dynamics

Louis-Philippe Morency, University of Southern California; Daniel Gatica-Perez, IDIAP Research Institute; Nigel Ward, University of Texas, El Paso

Monte Carlo Methods for Bayesian Inference in Modern Day Applications

Ryan Adams, Harvard University; Mark Girolami, University of Glasgow; Iain Murray, University of Edinburgh

Networks Across Disciplines: Theory and Applications

Edoardo Airoldi, Harvard University; Anna Goldenberg, University of Toronto; Jure Leskovec, Stanford University; Quaid Morris, University of Toronto

New Directions in Multiple Kernel Learning

Marius Kloft, UC Berkeley / TU Berlin; Ulrich Rueckert, UC Berkeley; Cheng Soon Ong, National ICT Australia; Alain Rakotomamonjy, LITIS, Univ de Rouen; Soeren Sonnenburg, TomTom; Francis Bach, INRIA - Ecole Normale Superieure

Numerical Mathematics Challenges in Machine Learning

Matthias Seeger, Ecole Polytechnique Federale de Lausanne; Suvrit Sra, Max Planck Institute for Intelligent Systems

Optimization for Machine Learning

Suvrit Sra, Max Planck Institute for Intelligent Systems; Sebastian Nowozin, Microsoft Research Cambridge; Stephen Wright, University of Wisconsin-Madison

Practical Application of Sparse Modeling: Open Issues and New Directions

Irina Rish, IBM T.J. Watson Research Center; Alexandru Niculescu-Mizil, NEC Laboratories America; Guillermo Cecchi, IBM Research; Aurelie Lozano, IBM T.J. Watson Research Center

Predictive Models in Personalized Medicine

Faisal Farooq, Siemens Medical Solutions, USA Inc.; Glenn Fung, Siemens Healthcare; Romer Rosales, Siemens Healthcare; Shipeng Yu, Siemens Healthcare; Jude Shavlik, University of Wisconsin at Madison; Balaji Krishnapuram, Siemens Medical Solutions USA, Inc.; Raju Kucherlapati, Department of Medicine, Harvard University

Robust Statistical Learning

Pradeep Ravikumar, University of Texas, Austin; Constantine Caramanis, The University of Texas; Sujay Sanghavi, University of Texas, Austin

Tensors, Kernels, and Machine Learning

Tamara Kolda, Sandia National Laboratories; Vicente Malave, University of California, San Diego; David Gleich, Sandia National Laboratories; Johan Suykens, K.U. Leuven; Marco Signoretto, KULeuven; Andreas Argyriou, Toyota Technological Institute

Transfer Learning Via Rich Generative Models.

Russ Salakhutdinov, University of Toronto; Ryan Adams, Harvard University; Josh Tenenbaum, Massachusetts Institute of Technology; Zoubin Ghahramani, University of Cambridge & CMU; Tom Griffiths, University of California, Berkeley