Computer Science Colloquium
Domain Adaptation with Multiple Sources
Yishay Mansour
Friday, November 7, 2008 11:30 A.M.
Room 1302 Warren Weaver Hall
251 Mercer Street
New York, NY 100121110
Directions: http://cs.nyu.edu/csweb/Location/directions.html
Colloquium Information: http://cs.nyu.edu/csweb/Calendar/colloquium/index.html
Host
Mehryar Mohri mohri@cs.nyu.edu, (212) 9983200
Synopsis
We will discuss problems that arise in machine learning regarding domain
adaptation. The motivation for the domain adaptation problem is to be
able to generalize from domains which are given, to new domains. The
practical motivation is to avoid the need to relearn from scratch a
good hypothesis for each domain.
In this talk we will concentrate on a theoretical analysis on a very
specific domain adaptation problem with multiple sources, specified as
follows. For each source domain, the distribution over the input points
as well as a hypothesis with error at most epsilon are given. Our
problem consists of combining these hypotheses to derive a
hypothesis with small error with respect to the target domain. We
present several theoretical results relating to this problem. In
particular, we prove that standard convex combinations of the source
hypotheses may in fact perform very poorly and that, instead,
combinations weighted by the source distributions benefit from
favorable theoretical guarantees. Our main result shows that,
remarkably, for any fixed target function, there exists a distribution
weighted combining rule that has a loss of at most epsilon with
respect to any target mixture of the source distributions. We further
generalize the setting from a single target function to multiple
consistent target functions and show the existence of a combining rule
with error at most 3*epsilon. Finally, we report some empirical
results for a multiple source adaptation problem with a realworld dataset.
This is joint work with M. Mohri and A. Rostamizadeh.
Bio
Prof. Yishay Mansour has done his PhD studies at MIT, performed a
postdoctoral in Harvard and worked in IBM T. J. Watson Research
Center. Since 1992 he is at TelAviv University, where he is currently a
Professor of Computer Science and has serves as the head of the School
of Computer Science during 20002002. Prof. Mansour has held visiting
positions with Bell Labs, AT&T research Labs, and IBM. Currently he is
visiting Google Inc. in New York.
Prof. Mansour has published over 50 journal papers and over 100
proceeding paper in many various areas of computer science with
special emphasis on communication networks and machine learning. Prof.
Mansour is currently an associate editor in a number of
distinguished journals (ACM Transactions on Algorithms, Machine
Learning Journal, Journal of Machine Learning Research, Mathematics of
Operations Research, International Journal of Game Theory, and Siam J.
on Computing) and has been on numerous conference program
committees. He was both the program chair of COLT (1998) and served on
the COLT steering committee. He has supervised over a dozen graduate
students in various areas including communication networks, machine
learning and algorithm design.
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