[Theory] REMINDER: 5/13 TTIC Colloquium: Amy Greenwald, Brown University

[Mary Marre via Theory]

*When:*         Monday, May 13, 2024 at* 11:00** am** CT   *


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

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*   *via panopto: **livestream*
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a6598a3d-2998-446d-ba52-b16a015533d8>

*                        *limited access: see info below*



*Who: *         Amy Greenwald, Brown University
------------------------------

*Title:*          Solving Games Forwards and Backwards
*Abstract:* Computing equilibria in games is a problem of great interest in
both economics and computer science. We present a min-max formulation of
this problem, in which the minimizer seeks an equilibrium solution for the
game, while the maximizer seeks to find fault with the proposed solutions.
We call this problem the “forward” problem. In the “backwards” problem, we
are instead given an equilibrium and a parameterized game, and we are
interested in inferring a game from that equilibrium: i.e., identifying the
game parameters that induce the observed equilibrium. We consider the
backwards problem under weaker and weaker assumptions, ranging from inverse
multiagent planning to inverse multiagent learning and beyond, for each of
which we present min-max formulations. While it can be difficult to ensure
that the min-max formulation of a forward problem is convex-concave, and
thus amenable to standard solutions (e.g., gradient descent ascent), we
find that it is easier to ensure that the min-max formulation of a backward
problem is convex-concave. We close by discussing avenues for solving
non-convex non-concave min-max optimization problems.

Joint work with Denizalp Goktas and Sadie Zhao

*Bio:* Amy Greenwald is Professor of Computer Science at Brown University
in Providence, Rhode Island. Her research focuses on game-theoretic and
economic interactions among computational agents with applications to
automated bidding and negotiation in domains ranging from advertising
auctions to supply chains. She is also active in promoting diversity in
Computer Science, leading multiple K-12 initiatives in the Providence
public schools.

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

*Access to this livestream is limited to TTIC / UChicago (press panopto
link and sign in to your UChicago account with CNetID).



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, May 8, 2024 at 4:14 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*         Monday, May 13, 2024 at* 11:00** am** CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*   *via panopto: **livestream*
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a6598a3d-2998-446d-ba52-b16a015533d8>
>
> *                        *limited access: see info below*
>
>
>
> *Who: *         Amy Greenwald, Brown University
> ------------------------------
>
> *Title:*          Solving Games Forwards and Backwards
> *Abstract:* Computing equilibria in games is a problem of great interest
> in both economics and computer science. We present a min-max formulation of
> this problem, in which the minimizer seeks an equilibrium solution for the
> game, while the maximizer seeks to find fault with the proposed solutions.
> We call this problem the “forward” problem. In the “backwards” problem, we
> are instead given an equilibrium and a parameterized game, and we are
> interested in inferring a game from that equilibrium: i.e., identifying the
> game parameters that induce the observed equilibrium. We consider the
> backwards problem under weaker and weaker assumptions, ranging from inverse
> multiagent planning to inverse multiagent learning and beyond, for each of
> which we present min-max formulations. While it can be difficult to ensure
> that the min-max formulation of a forward problem is convex-concave, and
> thus amenable to standard solutions (e.g., gradient descent ascent), we
> find that it is easier to ensure that the min-max formulation of a backward
> problem is convex-concave. We close by discussing avenues for solving
> non-convex non-concave min-max optimization problems.
>
> Joint work with Denizalp Goktas and Sadie Zhao
>
> *Bio:* Amy Greenwald is Professor of Computer Science at Brown University
> in Providence, Rhode Island. Her research focuses on game-theoretic and
> economic interactions among computational agents with applications to
> automated bidding and negotiation in domains ranging from advertising
> auctions to supply chains. She is also active in promoting diversity in
> Computer Science, leading multiple K-12 initiatives in the Providence
> public schools.
>
> *Host: **Avrim Blum* <avrim at ttic.edu>
>
> *Access to this livestream is limited to TTIC / UChicago (press panopto
> link and sign in to your UChicago account with CNetID).
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL  60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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