Edoardo Airoldi, Harvard University; Jure Leskovec, Stanford University; Jon Kleinberg, Cornell University; Josh Tenenbaum, Massachusetts Institute of Technology

Applications for Topic Models: Text and Beyond

David Blei, Princeton University; Jordan Boyd-Graber, Computer Science; Jonathan Chang, Facebook; Katherine Heller, University of Cambridge; Hanna Wallach, University of Massachusetts Amherst

Approximate Learning of Large Scale Graphical Models

Ruslan Salakhutdinov, Massachusetts Institute of Technology; Amir Globerson, Hebrew University; David Sontag, Massachusetts Institute of Technology

Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain

Noah Goodman, Massachusetts Institute of Technology; Edward Vul, Massachusetts Institute of Technology; Tom Griffiths, University of California, Berkeley; Josh Tenenbaum, Massachusetts Institute of Technology

Clustering: Science or art? Towards principled approaches

Margareta Ackerman, University of Waterloo; Shai Ben-David, University of Waterloo; Avrim Blum, Carnegie Mellon University; Isabelle Guyon, Clopinet; Ulrike von Luxburg, Max Plank Institute for Biological Cybernetics; Robert Williamson, Australian National University; Reza Bosagh Zadeh, Carnegie Mellon University

Connectivity Inference in Neuroimaging

Karl Friston, University College London; Moritz Grosse-Wentrup, Max Planck Institute for Biological Cybernetics; Uta Noppeney, MPI Tuebingen; Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics

Deep Learning for Speech Recognition and Related Applications

Li Deng, Microsoft Corporation; Dong Yu, Microsoft Research; Geoffrey Hinton, University of Toronto

Discrete Optimization in Machine Learning: Submodularity, Polyhedra and Sparsity

Andreas Krause, California Institute of Technology; Pradeep Ravikumar, University of Texas, Austin; Jeff Bilmes, University of Washington

Grammar Induction, Representation of Language and Language Learning

Alex Clark, Royal Holloway University of London; Dorota Glowacka, University College London; John Shawe-Taylor, University College London; Yee Whye Teh, Gatsby Computational Neuroscience Unit, UCL; Chris Watkins, Royal Holloway University of London

Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View

Mauricio Alvarez, University of Manchester; Lorenzo Rosasco, Massachusetts Institute of Technology; Neil Lawrence, University of Manchester

Large-Scale Machine Learning: Parallelism and Massive Datasets

Alex Gray, Georgia Tech University; Alexander Smola, Yahoo! Research; Arthur Gretton, CMU; Joseph Gonzalez, Carnegie Mellon University; Carlos Guestrin, Carnegie Mellon University

Learning from Multiple Sources with Applications to Robotics

Barbara Caputo, Idiap Research Institute; Nicolo Cesa-Bianchi, Università degli Studi di Milano; David Hardoon, Institute for Infocomm Research; Gayle Leen, Helsinki University of Technology; Francesco Orabona, Idiap Research Institute; Jaakko Peltonen, Aalto University; Simon Rogers, University of Glasgow

Learning with Orderings

Tiberio Caetano, NICTA - Australian National University; Carlos Guestrin, Carnegie Mellon University; Jonathan Huang, Carnegie Mellon University; Risi Kondor, Caltech; Guy Lebanon, Georgia Tech; Marina Meila, University of Washington

Machine Learning in Computational Biology

Gal Chechik, Google; Tomer Hertz, Fred Hutchnison Cancer Research Center; William Noble, University of Washington; Yanjun Qi, NEC Labs America; Jean-Philippe Vert, ParisTech; Alexander Zien, LIFE Biosystems GmbH

Manifolds, sparsity, and structured models: When can low-dimensional geometry really help?

Richard Baraniuk, Rice University; Volkan Cevher, Rice University; Mark Davenport, Rice University; Piotr Indyk, Massachusetts Institute of Technology; Bruno Olshausen, UC Berkeley; Michael Wakin, Colorado School of Mines

Nonparametric Bayes

Dilan Gorur, UC Irvine; Francois Caron, INRIA Bordeaux-Sud-Ouest; Yee Whye Teh, Gatsby Computational Neuroscience Unit, UCL; David Dunson, ; Zoubin Ghahramani, University of Cambridge & CMU; Michael Jordan, University of California

Normative electrophysiology: Explaining cellular properties of neurons from first principles

Jean-Pascal Pfister, Cambridge University; Mate Lengyel, University of Cambridge

Optimization for Machine Learning

Sebastian Nowozin, Microsoft Research Cambridge; Suvrit Sra, Max Planck Institute for Biological Cybernetics; S.V.N Vishwanthan, Purdue University; Stephen Wright, University of Wisconsin-Madison

Probabilistic Approaches for Control and Robotics

Marc Deisenroth, University of Washington; Hilbert Kappen, Radboud University; Emo Todorov, University of Washington; Duy Nguyen-Tuong, Max-Planck Institute for Biological Cybernetics; Carl Edward Rasmussen, University of Cambridge; Jan Peters, Max-Planck Institute of Biological Cybernetics

Statistical Machine Learning for Visual Analytics

Guy Lebanon, Georgia Tech; Fei Sha, University of Southern California

Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing

Stephane Canu, INSA Rouen, LITIS; Olivier Cappe, TELECOM ParisTech; Arthur Gretton, CMU; Zaid Harchaoui, Carnegie Mellon University; Alain Rakotomamonjy, LITIS, Univ de Rouen; Jean-Philippe Vert, ParisTech

The Curse of Dimensionality Problem: How Can the Brain Solve It?

Simon Haykin, Mc Master University; Terrence Sejnowski, Salk Institute; Steven Zucker, Yale University

The Generative and Discriminative Learning Interface

Simon Lacoste-Julien, University of Cambridge; Percy Liang, UC Berkeley; Guillaume Bouchard, Xerox Research Center Europe

Transfer Learning for Structured Data

Sinno Jialin Pan, Hong Kong University of Science and Technology; Ivor Tsang, Nanyang Technological University; Le Song, Carnegie Mellon University; Karsten Borgwardt, MPIs Tuebingen; Qiang Yang, Hong Kong University of Sci and Tech

Understanding Multiple Kernel Learning Methods

Gert Lanckriet, U.C. San Diego; Brian McFee, University of California, San Diego; Francis Bach, INRIA - Ecole Normale Superieure; Nathan Srebro, TTI-Chicago