Learning and Domain Adaptation
(invited lecture for ALT 2009)
Author: Yishay Mansour
Affiliation:
School of Computer Science,
Tel Aviv University, Israel
Abstract.
Domain adaptation is a fundamental learning problem where one wishes
to use labeled data from one or several source domains to learn a
hypothesis performing well on a different, yet related, domain for
which no labeled data is available. This generalization across domains
is a very significant challenge for many machine learning applications
and arises in a variety of natural settings, including NLP tasks
(document classification, sentiment analysis, etc.), speech
recognition (speakers and noise or environment adaptation) and Face
recognition (different lighting conditions, different population
composition).
The learning theory community has only recently started to analyze
domain adaptation problems. In the talk, I will overview some recent
theoretical models and results regarding domain adaptation.
This is based on joint work with Mehryar Mohri and Afshin
Rostamizadeh.
Biography:
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 Tel-Aviv University, where he is
currently
a Professor of Computer Science and has served as the head of
the School of Computer Science during 2000-2002. Prof. Mansour
has held visiting positions with Bell Labs, AT&T research Labs,
and IBM Research, and recently at Google Research in New York
during 2007-2009.
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 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.
©Copyright 2009 Author
|