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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: Unbiased Learning from Biased Feedback\nSpeaker:
	 Thorsten Joachims\nAbstract: Logged user interactions are one of the
	 most ubiquitous forms of data available\, as they can be recorded from a
	 variety of systems (e.g.\, search engines\, recommender systems\, ad
	 placement) at little cost. Naively using this data\, however\, is prone
	 to failure. A key problem lies in biases the system injects into the
	 logs by influencing where we will receive feedback (e.g.\, more clicks
	 at the top of the search ranking). To overcome the bias problem\, the
	 talk lays out a research agenda around counterfactual inference
	 techniques that can make learning algorithms robust to bias. This makes
	 log data accessible to a broad range of learning algorithms\, from
	 Conditional Random Fields to Deep Networks.\n\nBio:\nThorsten Joachims
	 is a Professor in the Department of Computer Science and the Department
	 of Information Science at Cornell University. His research interests
	 center on a synthesis of theory and system building in machine
	 learning\, with applications in search\, recommendation\, and language
	 technology. His past research focused on counterfactual and causal
	 inference\, support vector machines\, text classification\, structured
	 output prediction\, convex optimization\, learning to rank\, learning
	 with preferences\, and learning from implicit feedback. He is an ACM
	 Fellow\, AAAI Fellow\, and Humboldt Fellow.
LOCATION:Gates 122
UID:2018-10-16
STATUS:TENTATIVE
DTSTART:20181016T160000Z
DTEND:20181016T170000Z
LAST-MODIFIED:20181015T193502Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T171451Z
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