[AI Seminar] ai-seminar-announce Digest, Vol 76, Issue 8

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH
1507.

On Sat, Sep 30, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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>    1.  AI Seminar sponsored by Apple -- Nihar B. Shah --        October 03
>       (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 30 Sep 2017 03:07:09 -0700
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Subject: [AI Seminar] AI Seminar sponsored by Apple -- Nihar B. Shah
>         --      October 03
> Message-ID:
>         <CABzq7eqU0OA2Ekk+qf7CmzHmvgZ2mOPSMzPNGzFuinre2=
> RxRA at mail.gmail.com>
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> 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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