[Theory] Reminder: 2/9 Talks at TTIC: Chara Podimata, Harvard University

[Mary Marre]

When:        Wednesday, February 9th at *10:30 am CT*

*Where:       *Talk will be given *live, in-person* at

                    TTIC, 6045 S. Kenwood Avenue

                    5th Floor, Room 530

*Virtually:*    Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_TCSBgaLhSiWe2B23x4VnPQ>*)


*Who: *         Chara Podimata, Harvard University


*Title*: Incentive-Aware Machine Learning for Decision Making

*Abstract*: As machine learning algorithms are increasingly being deployed
for consequential decision making (e.g., loan approvals, college
admissions, probation decisions etc.) humans are trying to strategically
change the data they feed to these algorithms in an effort to obtain better
decisions for themselves. If the deployed algorithms do not take these
incentives into account they risk creating policy decisions that are
incompatible with the original policy’s goal.

In this talk, I will give an overview of my work on Incentive-Aware Machine
Learning for Decision Making, which studies the effects of strategic
behavior both to institutions and society as a whole and proposes ways to
robustify machine learning algorithms to strategic individuals. I will
first explain the goals of the different stakeholders (institution,
individual, society) in these settings in a unified way and show the
various settings I have worked on that belong in the incentive-aware
machine learning area such as incentive-compatible algorithms for linear
regression and online prediction with expert advice, strategic
classification, learning in auctions, and dynamic pricing. I will conclude
by looking at the problem from a societal lens and discuss the tension that
arises between having decision-making algorithms that are fully transparent
and incentive-aware.


*Bio*: Chara is a final year PhD student at Harvard, where she is advised
by Yiling Chen. Her research is generously supported by a Microsoft
Dissertation Grant and a Siebel Scholarship. During her PhD, she interned
twice for MSR NYC (mentored by Jennifer Wortman Vaughan and Aleksandrs
Slivkins) and once for Google Research NYC (mentored by Renato Paes Leme).
She has given tutorials related to strategic learning at EC20 and FAccT21.
Outside of research, she spends her time adventuring with her pup, Terra.

*Host:* *Avrim Blum* <avrim at ttic.edu>


Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, Feb 2, 2022 at 8:57 PM Mary Marre <mmarre at ttic.edu> wrote:

> When:        Wednesday, February 9th at *10:30 am CT*
>
> *Where:       *Talk will be given *live, in-person* at
>
>                     TTIC, 6045 S. Kenwood Avenue
>
>                     5th Floor, Room 530
>
> *Virtually:*    Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_TCSBgaLhSiWe2B23x4VnPQ>*
> )
>
>
> *Who: *         Chara Podimata, Harvard University
>
>
> *Title*: Incentive-Aware Machine Learning for Decision Making
>
> *Abstract*: As machine learning algorithms are increasingly being
> deployed for consequential decision making (e.g., loan approvals, college
> admissions, probation decisions etc.) humans are trying to strategically
> change the data they feed to these algorithms in an effort to obtain better
> decisions for themselves. If the deployed algorithms do not take these
> incentives into account they risk creating policy decisions that are
> incompatible with the original policy’s goal.
>
> In this talk, I will give an overview of my work on Incentive-Aware
> Machine Learning for Decision Making, which studies the effects of
> strategic behavior both to institutions and society as a whole and proposes
> ways to robustify machine learning algorithms to strategic individuals. I
> will first explain the goals of the different stakeholders (institution,
> individual, society) in these settings in a unified way and show the
> various settings I have worked on that belong in the incentive-aware
> machine learning area such as incentive-compatible algorithms for linear
> regression and online prediction with expert advice, strategic
> classification, learning in auctions, and dynamic pricing. I will conclude
> by looking at the problem from a societal lens and discuss the tension that
> arises between having decision-making algorithms that are fully transparent
> and incentive-aware.
>
>
> *Bio*: Chara is a final year PhD student at Harvard, where she is advised
> by Yiling Chen. Her research is generously supported by a Microsoft
> Dissertation Grant and a Siebel Scholarship. During her PhD, she interned
> twice for MSR NYC (mentored by Jennifer Wortman Vaughan and Aleksandrs
> Slivkins) and once for Google Research NYC (mentored by Renato Paes Leme).
> She has given tutorials related to strategic learning at EC20 and FAccT21.
> Outside of research, she spends her time adventuring with her pup, Terra.
>
> *Host:* *Avrim Blum* <avrim at ttic.edu>
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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