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Workshops II: Numerical Tools and Fast Algorithms for Massive Data Mining, Search Engines and Applications
October 22 - 26, 2007
Organizing Committee |
Scientific Overview |
Speaker List
Application/Registration |
Contact Us
Organizing Committee
Yann LeCun, Chair
(New York University)
Ming Gu
(University of California, Berkeley (UC Berkeley))
Piotr Indyk
(Massachusetts Institute of Technology)
Vladimir Rokhlin
(Yale University)
Sam Roweis
(University of Toronto)
Andrew Zisserman
(University of Oxford)
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Scientific Overview
The present era of science can be said to have begun with the appearance of computers and
large data sets. For more than a half century the tools of numerical analysis have required
repeated honing in the attempt to keep up with the explosion of data and information
generated in our technological world. The next step in evolution has been the rise of
massive networks (e.g. the internet and world wide web) and this has led to a concomitant
demand for new, fast algorithms for solutions of problems related to the page weight
algorithm, webcrawlers, etc. Increasingly, large data sets are no longer restricted to
classical scientific domains, but arise in virtually all fields, including finance,
economics, social networks, law, and the humanities. What is now beginning to emerge is
the next generation of numerical algorithms that can be used to sort, order, or otherwise
extract knowledge in a wide variety of situations. As the information sciences expand and
integrate with other disciplines, the need for these tools has become especially acute.
All of the modern, numerical problems encountered have the common feature that they require
scalable algorithms with robustness, i.e. good error estimates. The development of fast
algorithms in the period 1980 -2000 has laid the groundwork for today's challenges of
numerical linear algebra, but new methods are now needed. This workshop will bring together
researchers in various disciplines to discuss advances in the following topics:
- Deterministic and randomized algorithms for matrix approximation
- Analysis of dense matrices
- Fast algorithms for SVD solvers
- Algorithms for l0 and l1 approximation
- High precision randomized algorithms of linear algebra
- Interior point methods
- Relation of fast solvers to the Fast Multipole Method
- Manifold approximation
- Band-limited functions on data sets
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Invited Speakers
Michael Berry
(University of Tennessee)
Dan Boley
(University of Minnesota, Twin Cities)
Leon Bottou
(NEC Research Institute)
Chris Burges
(Microsoft Research)
Oliver Chapelle
(Yahoo! Research)
Inderjit Dhillon
(University of Texas at Austin)
Rob Fergus
(New York University)
Andrew Fitzgibbon
(Microsoft Research)
Kristen Grauman
(University of Texas at Austin)
Mark Green
(Institute for Pure and Applied Mathematics)
Bruce Hendrickson
(Sandia National Laboratories)
Alfred Inselberg
(Tel Aviv University)
Michael Isard
(Microsoft Research)
Peter Jones
(Yale University)
Tammy Kolda
(Sandia National Laboratories)
Svetlana Lazebnik
(University of North Carolina)
Yann LeCun
(New York University)
Till Quack
(Eidgenössische TH Zürich-Hönggerberg)
Vladimir Rokhlin
(Yale University)
Sam Roweis
(University of Toronto)
Ruslan Salakhutdinov
(University of Toronto)
Tamas Sarlos
(Yahoo! Research)
Yoram Singer
(Google Inc.)
Mark Tygert
(Yale University)
Wotao Yin
(Rice University)
Andrew Zisserman
(University of Oxford)
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Contact Us:
Institute for Pure and Applied Mathematics (IPAM)
Attn: SEWS2
460 Portola Plaza
Los Angeles CA 90095-7121
Phone: 310 825-4755
Fax: 310 825-4756
Email: 
Website:
http://www.ipam.ucla.edu/programs/sews2/
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