[auton-users] NIPS*2007 Workshop on Efficient Machine Learning

[Dan Pelleg]

           NIPS*2007 Workshop on Efficient Machine Learning
        Overcoming Computational Bottlenecks in Machine Learning
               Whistler, Canada, December 7-8, 2007
                  http://bigml.wikispaces.com/cfp


Overview
--------

The ever increasing size of available data to be processed by machine
learning algorithms has yielded several approaches, from online algorithms
to parallel and distributed computing on multi-node clusters. Nevertheless,
it is not clear how modern machine learning approaches can either cope with
such parallel machineries or take into account strong constraints regarding
the available time to handle training and/or test examples. This workshop
will explore two alternatives:

     * modern machine learning approaches that can handle real time
processing at train and/or at test time, under strict computational constraints
(when the flow of incoming data is continuous and needs to be handled)

     * modern machine learning approaches that can take advantage of new
commodity hardware such as multicore, GPUs, and fast networks.

This two-day workshop aims to set the agenda for future advancements by
fostering a discussion of new ideas and methods and by demonstrating the
potential uses of readily-available solutions. It will bring together both
researchers and practitioners to offer their views and experience in
applying machine learning to large scale learning.

Topics of Interest
------------------

     * efficient parallelization of machine learning algorithms and
algorithms
       that make use of new hardware architectures
     * sub-linear training algorithms for virtually infinite datasets
     * new online boosting, online kernel, and other efficient non-linear
       online training algorithms
     * efficient feature extraction for classification and detection
     * adapted structures for very large number of features per example
     * evolving under strict time/space constraints
     * coarse-to-fine and "focusing" algorithms for detection

Submission Procedure
--------------------

We encourage the submissions of extended abstract. The suggested abstract
length is about 2 pages. The invited speakers will be allocated between 40 and 60
minutes, while the authors of the accepted abstracts will be allocated
between 30 and 40 minutes to present their work (to be determined according to
submissions). In addition, the abstracts will be available to a broader
audience on the dedicated web site. The authors should submit their extended
abstract to bigml.nips at gmail.com in pdf. An email confirming the reception of the
submission will be sent by the organizers.

Important Dates
---------------

     * Aug 28: Workshop announcement / call for abstracts
     * Oct 12: Abstract submission deadline
     * Nov 1: Notification of acceptance
     * Dec 7 and 8: Workshop

Invited Speakers
----------------

     * Yali Amit, University of Chicago
     * Yoshua Bengio, University of Montreal
     * Michael Burl, NASA JPL
     * Corinna Cortes, Google
     * Dennis DeCoste, Microsoft
     * Don Geman, John Hopkins University
     * Dan Pelleg and Elad Yom-Tov, IBM Research
     * Yann LeCun, New York University
     * Srinivasan Parthasarathy, Ohio State University
     * Nicol N. Schraudolph, National ICT Australia

Organizers
----------

     * Samy Bengio, Google
     * Corinna Cortes, Google
     * Dennis DeCoste, Microsoft Live Labs
     * Francois Fleuret, IDIAP Research Institute
     * Ramesh Natarajan, IBM T.J. Watson Research Lab
     * Edwin Pednault, IBM T.J. Watson Research Lab
     * Dan Pelleg, IBM Haifa Research Lab
     * Elad Yom-Tov, IBM Haifa Research Lab

Other Members of the Programme Committee
----------------------------------------

     * Yali Amit, University of Chicago
     * Gilles Blanchard, Fraunhofer Institut FIRST, Berlin
     * Ronan Collobert, NEC
     * Yves Grandvalet
     * IDIAP Research Institute
     * Jiri Matas, Czech Technical University, Prague
     * Sam Roweis, Google