[Sds-seminars] [Sds-announce] Fwd: [YINS] 10/20: Avrim Blum, "On learning in the presence of biased data and strategic behavior"

[Dan Spielman]

---------- Forwarded message ---------
From: Hau, Emily <emily.hau at yale.edu>
Date: Mon, Oct 18, 2021 at 4:41 PM
Subject: [YINS] 10/20: Avrim Blum, "On learning in the presence of biased
data and strategic behavior"
To: yins at mailman.yale.edu <yins at mailman.yale.edu>


[image: cid:image001.png at 01D7BF65.7BBD3AC0]


*YINS Seminar: “On learning in the presence of biased data and strategic
behavior”*

*Speaker: Avrim Blum*
*Professor and Chief Academic Officer*

* Toyota Technological Institute at Chicago http://www.ttic.edu/blum
<http://www.ttic.edu/blum>*

*Wednesday, October 20, 2021 - 12:00pm*

*Talk summary: *In this talk I will discuss two lines of work involving
learning in the presence of biased data and strategic behavior.  In the
first, we ask whether fairness constraints on learning algorithms can
actually improve the accuracy of the classifier produced, when training
data is unrepresentative or corrupted due to bias.   Typically, fairness
constraints are analyzed as a tradeoff with classical objectives such as
accuracy.  Our results here show there are natural scenarios where they can
be a win-win, helping to improve overall accuracy.  In the second line of
work we consider strategic classification: settings where the entities
being measured and classified wish to be classified as positive (e.g.,
college admissions) and will try to modify their observable features if
possible to make that happen.   We consider this in the online setting
where a particular challenge is that updates made by the learning algorithm
will change how the inputs behave as well.

*To participate:*
Join from PC, Mac, Linux, iOS or Android: https://yale.zoom.us/j/93612337599
    Or Telephone：203-432-9666 (2-ZOOM if on-campus) or 646 568 7788
    Meeting ID: 936 1233 7599
    International numbers available: https://yale.zoom.us/u/abMfmltZ0O

*Speaker bio: *Avrim Blum received his BS, MS, and PhD from MIT in 1987,
1989, and 1991 respectively.  He then served on the faculty in the Computer
Science Department at Carnegie Mellon University from 1992 to 2017. In 2017
he joined the Toyota Technological Institute at Chicago as Chief Academic
Officer.

Blum’s main research interests are in Theoretical Computer Science and
Machine Learning, including Machine Learning Theory, Approximation
Algorithms, Algorithmic Game Theory, and Database Privacy, as well as
connections among them. Some current specific interests include multi-agent
and distributed learning, fairness, learning systems that know when they
don’t know, and the design of incentive systems. He has served as Program
Chair for the IEEE Symposium on Foundations of Computer Science (FOCS), the
Innovations in Theoretical Computer Science Conference (ITCS), and the
Conference on Learning Theory (COLT).  He has served as Chair of the ACM
SIGACT Committee for the Advancement of Theoretical Computer Science and on
the SIGACT Executive Committee.  Blum is recipient of the AI Journal
Classic Paper Award, the ICML/COLT 10-Year Best Paper Award, the Sloan
Fellowship, the NSF National Young Investigator Award, and the Herbert
Simon Teaching Award, and he is a Fellow of the ACM.

*Upcoming YINS Seminars:*



10/27/21 Yasaman Bahri (Google Brain)
<https://yins.yale.edu/event/yins-industry-seminar-yasaman-bahri-google>

11/3/21 Marinka Zitnik (Harvard)
<https://yins.yale.edu/event/yins-seminar-marinka-zitnik-harvard>

11/10/21 Karen Singh (Princeton)
<https://yins.yale.edu/event/yins-seminar-karan-singh-princeton>



Click here to subscribe to YINS emails.
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Emily E. H. Hau | Director, Programs and Partnerships

*Yale Institute for Network Science*

*Yale University*

*17 Hillhouse Avenue | Room 341 | New Haven, CT 06511*

c: (203) 273-7886

*emily.hau at yale.edu <emily.hau at yale.edu>*


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