Learning and domain adaptation

Yishay Mansour*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 talk is based on joint works with Mehryar Mohri and Afshin Rostamizadeh.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 20th International Conference, ALT 2009, Proceedings
Number of pages3
StatePublished - 2009
Event20th International Conference on Algorithmic Learning Theory, ALT 2009 - Porto, Portugal
Duration: 3 Oct 20095 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5809 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Algorithmic Learning Theory, ALT 2009


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