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                          CALL FOR PAPERS

       Low-rank Matrix Approximation for Large-scale Learning
              NIPS 2010 Workshop, Whistler, Canada
                        December 11, 2010

        http://www.eecs.berkeley.edu/~ameet/low-rank-nips10/

             Submission Deadline: October 31, 2010
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Overview

Today's data-driven society is full of large-scale datasets. In the context of machine learning, these datasets are often represented by large matrices representing either a set of real-valued features for each point or pairwise similarities between points. Hence, modern learning problems in computer vision, natural language processing, computational biology, and other areas often face the daunting task of storing and operating on matrices with thousands to millions of entries. An attractive solution to this problem involves working with low-rank approximations of the original matrix. Low-rank approximation is at the core of widely used algorithms such as Principle Component Analysis, Multidimensional Scaling, Latent Semantic Indexing, and manifold learning. Furthermore, low-rank matrices appear in a wide variety of applications including lossy data compression, collaborative filtering, image processing, text analysis, matrix completion and metric learning.

The NIPS workshop on "Low-rank Matrix Approximation for Large-scale Learning" aims to survey recent work on matrix approximation with an emphasis on usefulness for practical large-scale machine learning problems. We aim to provide a forum for researchers to discuss several important questions associated with low-rank approximation techniques. The workshop will begin with an introductory talk and will include invited talks by Emmanuel Candes (Stanford), Ken Clarkson (IBM Almaden) and Petros Drineas (RPI). There will also be several contributed paper talks as well as poster session for contributed papers.

We encourage submissions exploring the impact of low-rank methods for large-scale machine learning in the form of new algorithms, theoretical advances and/or empirical results. We also welcome work on related topics that motivate additional interesting scenarios for use of low-rank approximations for learning tasks. Some specific areas of interest include randomized low-rank approximation techniques, the effect of data heterogeneity on randomization, performance of various low-rank methods for large-scale tasks and the tradeoff between numerical precision and time/space efficiency in the context of machine learning performance, e.g., classification or clustering accuracy.

Submission Guidelines

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS latex style. Style files and formatting instructions can be found at http://nips.cc/PaperInformation/StyleFiles. Submisssions must be in PDF format. Authors names and affiliations should be included, as 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 PDF submission by email to submit.lowrank@gmail.com by October 31. Notifications will be given on or before November 15. Topics that were recently published or presented elsewhere are allowed, provided that the extended abstract mentions this explicitly.

Organizers

Arthur Gretton (UCL-Gatsby), Michael Mahoney (Stanford), Mehryar Mohri (NYU, Google Research), Ameet Talwalkar (Berkeley)

Program Committee

Alexandre d'Aspremont (Princeton), Christos Boutsidis (Rensselear Polytechnic Institute), Kamilika Das (NASA Ames Research Center), Maryam Fazel (Washington), Michael I. Jordan (Berkeley), Sanjiv Kumar (Google Research), James Kwok (Hong Kong University of Science and Technology), Gunnar Martinsson (Colorado at Boulder)