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.

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