[AI Seminar] Update: Talk by Omer Reingold on March 29

[Ariel Procaccia]

Catherine Copetas tells me that the location has changed to Hamerschlag
Hall 1107.

Cheers,
Ariel

On Tue, Mar 13, 2018 at 4:04 PM, Ariel Procaccia <arielpro at cs.cmu.edu>
wrote:

> Of possible interest:
>
> *ECE Seminar: *Calibration for the (computationally-identifiable) masses
>
> *Starts at: *March 29, 2018 4:30 PM
>
> *Ends at: *6:00 PM
>
> *Location: *DH A302
>
> *Speaker: *Dr. Omer Reingold
>
> *Affiliation: *Stanford University
>
> *Refreshments provided: *Yes
>
> Link to Abstract
> <http://www.ece.cmu.edu/news/calendar/2018/03/ece-grad-seminar-reingold.pdf>
>
>   iCalendar
> <http://www.ece.cmu.edu/news/calendar/2018/03/ece-grad-seminar-reingold.ics>
>
> *Details:*
>
> Abstract:
> As algorithms increasingly inform and influence decisions made about
> individuals, it becomes increasingly important to address concerns that
> these algorithms might be discriminatory. The output of an algorithm can be
> discriminatory for many reasons, most notably: (1) the data used to train
> the algorithm might be biased (in various ways) to favor certain
> populations over others; (2) the analysis of this training data might
> inadvertently or maliciously introduce biases that are not borne out in the
> data. This work focuses on the latter concern.
>
> We develop and study multicalbration as a new measure of algorithmic
> fairness that aims to mitigate concerns about discrimination that is
> introduced in the process of learning a predictor from data.
> Multicalibration guarantees accurate (calibrated) predictions for every
> subpopulation that can be identified within a specified class of
> computations. We think of the class as being quite rich, in particular it
> can contain many and overlapping subgroups of a protected group.
>
> We show that in many settings this strong notion of protection from
> discrimination is both attainable and aligned with the goal of obtaining
> accurate predictions. Along the way, we present new algorithms for learning
> a multicalibrated predictor, study the computational complexity of this
> task, and draw new connections to computational learning models such as
> agnostic learning.
>
> Joint work with Ursula Hebert-Johnson, Michael P. Kim and Guy Rothblum
>
> Bio:
> Omer Reingold is a Professor of Computer Science at Stanford University.
> Past positions include Samsung Research America, the Weizmann Institute of
> Science, Microsoft Research, the Institute for Advanced Study in Princeton,
> NJ and AT&T Labs. His research is in the Foundations of Computer Science
> and most notably in Computational Complexity and the Foundations of
> Cryptography with emphasis on randomness, derandomization and explicit
> combinatorial constructions. He has a keen interest in the societal impact
> of computation. He is an ACM Fellow and among his distinctions are the 2005
> Grace Murray Hopper Award and the 2009 Gödel Prize.
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20180313/9dcbf177/attachment.html>