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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Thorsten Joachims
DESCRIPTION:Title: Learning from User Interactions through
	 Interventions\nSpeaker: Thorsten Joachims\nAbstract: The ability to
	 learn from user interactions can give systems access to unprecedented
	 amounts of world knowledge. This is already evident in search engines\,
	 recommender systems\, and electronic commerce\, and other applications
	 are likely to follow in the near future (e.g.\, education\, smart
	 homes). More generally\, the ability to learn from user interactions
	 promises pathways for solving knowledge-intensive tasks ranging from
	 natural language understanding to autonomous robotics.\n \nLearning from
	 user interactions\, however\, means learning from data that does not
	 necessarily fit the assumptions of the standard machine learning models.
	 Since interaction data consists of the choices that humans make\, it has
	 to be interpreted with respect to how humans make decisions\, which is
	 influenced by the decision context and constraints like human motivation
	 and human abilities.\nIn this talk\, I argue that we need learning
	 approaches that explicitly model user-interaction data as the result of
	 human decision making.\n \nTo this effect\, the talk explores how
	 integrating micro-economic models of human behavior into the learning
	 process leads to new learning algorithms that have provable guarantees
	 under verifiable assumptions and to learning systems that perform
	 robustly in practice. These findings imply that the design space of such
	 human-interactive learning systems encompasses not only the machine
	 learning algorithm itself\, but also the design of the interaction under
	 an appropriate model of user behavior.
LOCATION:Gates 122
UID:2015-03-03
STATUS:CONFIRMED
DTSTART:20150303T170000Z
DTEND:20150303T180000Z
LAST-MODIFIED:20150302T165314Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T170049Z
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