SCS Faculty Candidate

Wednesday, March 8, 2017 - 10:00am to 11:30am


ASA Conference Room 6115 Gates Hillman Center


NIHAR B. SHAH, Ph.D. Candidate

Learning from 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.—Nihar B. Shah is a PhD candidate 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 masters thesis in ECE. His research interests include statistics and machineFaculty Host: Ariel ProcacciaMachine Learning/Computer Science/Electrical & Computer Engineering

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CSD Faculty Candidate