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

[Mary Marre via Theory]

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 Fri, May 10, 2024 at 7:54 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>*
>
>
> 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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