[AI Seminar] AI Seminar sponsored by Apple -- Nihar B. Shah -- October 03

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you next Tuesday, October 03, at noon in NSH 1507
(unusual place) for AI Seminar sponsored by Apple. To learn more about the
seminar series, please visit the AI Seminar webpage
<http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Professor Nihar B. Shah <http://www.cs.cmu.edu/~nihars/> will
give the following talk:

Title: Learning from People

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 an Assistant Professor in the Machine Learning and
Computer Science departments at CMU. He is a recipient of the 2017 David J.
Sakrison memorial prize from EECS Berkeley for a "truly outstanding and
innovative PhD thesis", 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 2010.
His research interests include statistics, machine learning, and game
theory, with a current focus on applications to learning from people.
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