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IDSS Special Seminars

Learning from People

March 13, 2017 @ 3:00 pm - 4:30 pm

Nihar Shah (University of California, Berkeley)

34-401 (Grier Room)

Abstract:
Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of “permutation-based” models that are considerably richer than classical models, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge, I present a class of “multiplicative” incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.

Bio:
Nihar B. Shah is a PhD candidate in the EECS department at the University of California, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, and the SVC Aiya Medal from the Indian Institute of Science for the best master’s thesis in the department. His research interests include statistics and machine learning, with a current focus on applications to learning from people.

Details

Date:
March 13, 2017
Time:
3:00 pm - 4:30 pm
Event Category:

Venue

34-401 (Grier Room)
The Stata Center (34-401)
50 Vassar Street, Cambridge, 02139 United States

Other

Speaker Name(s)
Nihar Shah
Speaker Affiliation
University of California, Berkeley

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