[Computational-biology] Call For Paper, Workshop on Cores, Clusters, and Clouds

John Langford via comp-bio%40net.bio.net (by jl from hunch.net)
Mon Aug 23 09:17:09 EST 2010


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             CALL FOR PAPERS

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

                 http://lccc.eecs.berkeley.edu/

           -- Submission Deadline: October 17, 2010 --

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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 
topics:

    * 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 <mailto: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.


            _Organizers:_


      Alekh Agarwal (UC Berkeley), 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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