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[Computational-biology] Call for papers: NIPS Workshop on Learning on Cores, Clusters, and Clouds

John Duchi via comp-bio%40net.bio.net (by jduchi from eecs.berkeley.edu)
Thu Oct 14 12:17:49 EST 2010


            CALL FOR PAPERS

            Learning on Cores, Clusters, and Clouds
             NIPS 2010 Workshop, Whistler, British Columbia, Canada


          -- Submission Deadline: October 17, 2010 --


In the current era of web-scale datasets, high throughput biology, and
multilanguage machine translation, modern datasets no longer fit on a single
computer and traditional machine learning algorithms often have
prohibitively long running times. Parallel and distributed machine learning
is no longer a luxury; it has become a necessity. Moreover, industry leaders
have already declared that clouds are the future of computing, and new
computing platforms such as Microsoft's Azure and Amazon's EC2 are bringing
distributed computing to the masses.

The machine learning community is reacting to this trend in computing by
developing new parallel and distributed machine learning techniques.
However, many important challenges remain unaddressed. Practical distributed
learning algorithms must deal with limited network resources, node failures
and nonuniform network latencies. In cloud environments, where network
latencies are especially large, distributed learning algorithms should take
advantage of asynchronous updates.

Many similar issues have been addressed in other fields, where distributed
computation is more mature, such as convex optimization and numerical
computation. We can learn from their successes and their failures.

The one day workshop on "Learning on Cores, Clusters, and Clouds" aims to
bring together experts in the field and curious newcomers, to present the
state-of-the-art in applied and theoretical distributed learning, and to map
out the challenges ahead. The workshop will include invited and contributed
presentations from leaders in distributed learning and adjacent fields.

We would like to invite short high-quality submissions on the following

   - Distributed algorithms for online and batch learning
   - Parallel (multicore) algorithms for online and batch learning
   - Computational models and theoretical analysis of distributed and
   parallel learning
   - Communication avoiding algorithms
   - Learning algorithms that are robust to hardware failures
   - Experimental results and interesting applications

   Interesting submissions in other relevant topics not listed above are
   welcome too. Due to the time constraints, most accepted submissions will be
   presented as poster spotlights.

   *Submission guidelines:*Submissions should be written as extended
   abstracts, no longer than 4 pages in the NIPS latex style. NIPS style files
   and formatting instructions can be found at
   http://nips.cc/PaperInformation/StyleFiles. The submissions should
   include the authors' name and affiliation since the review process will not
   be double blind. The extended abstract may be accompanied by an unlimited
   appendix and other supplementary material, with the understanding that
   anything beyond 4 pages may be ignored by the program committee. Please send
   your submission by email to submit.lccc from gmail.com before October 17 at
   midnight PST. Notifications will be given on or before Nov 7. Topics that
   were recently published or presented elsewhere are allowed, provided that
   the extended abstract mentions this explicitly; topics that were presented
   in non-machine-learning conferences are especially encouraged.

   Alekh Agarwal (UC Berkeley), Lawrence Cayton (MPI Tuebingen), Ofer Dekel
   (Microsoft), John Duchi (UC Berkeley), John Langford (Yahoo!)

   *Program Committee:*
   Ron Bekkerman (LinkedIn), Misha Bilenko (Microsoft), Ran Gilad-Bachrach
   (Microsoft), Guy Lebanon (Georgia Tech), Ilan Lobel (NYU), Gideon Mann
   (Google), Ryan McDonald (Google), Ohad Shamir (Microsoft), Alex Smola
   (Yahoo!), S V N Vishwanathan (Purdue), Martin Wainwright (UC Berkeley), Lin
   Xiao (Microsoft)

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