[Theory] NOW: 10/28 TTIC Colloquium: Nina Balcan, Carnegie Mellon University

[Mary Marre via Theory]

*When*:    Monday, October 28th from* 11**am - 12pm CT*

*Where*:   Talk will be given *live, in-person* at
               TTIC, 6045 S. Kenwood Avenue
               5th Floor, *Room 530*

*Virtually*: *join here
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=22efd68d-1145-48fc-9677-b2160151093f>*

*Who:  *    Nina Balcan, Carnegie Mellon University


*Title:      *Online learning in Stackelberg Security Games

*Abstract:* In a Stackelberg Security Game, a defender commits to a
randomized deployment of security resources, and an attacker best responds
by attacking a target that maximizes their utility. While algorithms for
computing an optimal strategy for the defender to commit to have been used
in several real-world applications, deployed applications require knowledge
about the utility function of the potential attacker. In this talk I will
describe an online learning approach for addressing this problem. We
consider algorithms that prescribe a randomized strategy for the defender
at each step against an adversarially chosen sequence of attackers and
obtain feedback on their choices. I will discuss online algorithms whose
regret (when compared to the best fixed strategy in hindsight) is sublinear
in the number of time steps. I will also consider an extension that handles
auxiliary contextual information that is often readily available to each
player (e.g. traffic patterns or weather conditions) and discuss what no
regret guarantees are possible in this even more realistic scenario.

*Bio: *Maria Florina Balcan is the Cadence Design Systems Professor of
Computer Science in the School of Computer Science at Carnegie Mellon
University. Her main research interests are machine learning, artificial
intelligence, theory of computing, and algorithmic game theory. She is a
Simons Investigator, an ACM Fellow, a Sloan Fellow, a Microsoft Research
New Faculty Fellow, and the recipient of the ACM Grace Murray Hopper Award,
NSF CAREER award, and several best paper awards. She  has given
distinguished lectures and invited keynote talks across different research
fields (including machine learning, information theory, mathematics, and
algorithmic game theory).  She has co-chaired major conferences in the
field: the Conference on Learning Theory (COLT) 2014, the International
Conference on Machine Learning (ICML) 2016, and Neural Information
Processing Systems (NeurIPS) 2020. She was also the general chair for the
International Conference on Machine Learning (ICML) 2021, a board member of
the International Machine Learning Society, and a co-organizer for the Simons
semester on Foundations of Machine Learning.

*Host:* Avrim Blum



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 Mon, Oct 28, 2024 at 10:51 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When*:    Monday, October 28th from* 11**am - 12pm CT*
>
> *Where*:   Talk will be given *live, in-person* at
>                TTIC, 6045 S. Kenwood Avenue
>                5th Floor, *Room 530*
>
> *Virtually*: *join here
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=22efd68d-1145-48fc-9677-b2160151093f>*
>
> *Who:  *    Nina Balcan, Carnegie Mellon University
>
>
> *Title:      *Online learning in Stackelberg Security Games
>
> *Abstract:* In a Stackelberg Security Game, a defender commits to a
> randomized deployment of security resources, and an attacker best responds
> by attacking a target that maximizes their utility. While algorithms for
> computing an optimal strategy for the defender to commit to have been used
> in several real-world applications, deployed applications require knowledge
> about the utility function of the potential attacker. In this talk I will
> describe an online learning approach for addressing this problem. We
> consider algorithms that prescribe a randomized strategy for the defender
> at each step against an adversarially chosen sequence of attackers and
> obtain feedback on their choices. I will discuss online algorithms whose
> regret (when compared to the best fixed strategy in hindsight) is sublinear
> in the number of time steps. I will also consider an extension that handles
> auxiliary contextual information that is often readily available to each
> player (e.g. traffic patterns or weather conditions) and discuss what no
> regret guarantees are possible in this even more realistic scenario.
>
> *Bio: *Maria Florina Balcan is the Cadence Design Systems Professor of
> Computer Science in the School of Computer Science at Carnegie Mellon
> University. Her main research interests are machine learning, artificial
> intelligence, theory of computing, and algorithmic game theory. She is a
> Simons Investigator, an ACM Fellow, a Sloan Fellow, a Microsoft Research
> New Faculty Fellow, and the recipient of the ACM Grace Murray Hopper Award,
> NSF CAREER award, and several best paper awards. She  has given
> distinguished lectures and invited keynote talks across different research
> fields (including machine learning, information theory, mathematics, and
> algorithmic game theory).  She has co-chaired major conferences in the
> field: the Conference on Learning Theory (COLT) 2014, the International
> Conference on Machine Learning (ICML) 2016, and Neural Information
> Processing Systems (NeurIPS) 2020. She was also the general chair for the
> International Conference on Machine Learning (ICML) 2021, a board member of
> the International Machine Learning Society, and a co-organizer for the Simons
> semester on Foundations of Machine Learning.
>
> *Host:* Avrim Blum
>
>
>
>
> 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 Sun, Oct 27, 2024 at 3:39 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When*:    Monday, October 28th from* 11**am - 12pm CT*
>>
>> *Where*:   Talk will be given *live, in-person* at
>>                TTIC, 6045 S. Kenwood Avenue
>>                5th Floor, *Room 530*
>>
>> *Virtually*: *tba*
>>
>> *Who:  *    Nina Balcan, Carnegie Mellon University
>>
>>
>> *Title:      *Online learning in Stackelberg Security Games
>>
>> *Abstract:* In a Stackelberg Security Game, a defender commits to a
>> randomized deployment of security resources, and an attacker best responds
>> by attacking a target that maximizes their utility. While algorithms for
>> computing an optimal strategy for the defender to commit to have been used
>> in several real-world applications, deployed applications require knowledge
>> about the utility function of the potential attacker. In this talk I will
>> describe an online learning approach for addressing this problem. We
>> consider algorithms that prescribe a randomized strategy for the defender
>> at each step against an adversarially chosen sequence of attackers and
>> obtain feedback on their choices. I will discuss online algorithms whose
>> regret (when compared to the best fixed strategy in hindsight) is sublinear
>> in the number of time steps. I will also consider an extension that handles
>> auxiliary contextual information that is often readily available to each
>> player (e.g. traffic patterns or weather conditions) and discuss what no
>> regret guarantees are possible in this even more realistic scenario.
>>
>> *Bio: *Maria Florina Balcan is the Cadence Design Systems Professor of
>> Computer Science in the School of Computer Science at Carnegie Mellon
>> University. Her main research interests are machine learning, artificial
>> intelligence, theory of computing, and algorithmic game theory. She is a
>> Simons Investigator, an ACM Fellow, a Sloan Fellow, a Microsoft Research
>> New Faculty Fellow, and the recipient of the ACM Grace Murray Hopper Award,
>> NSF CAREER award, and several best paper awards. She  has given
>> distinguished lectures and invited keynote talks across different research
>> fields (including machine learning, information theory, mathematics, and
>> algorithmic game theory).  She has co-chaired major conferences in the
>> field: the Conference on Learning Theory (COLT) 2014, the International
>> Conference on Machine Learning (ICML) 2016, and Neural Information
>> Processing Systems (NeurIPS) 2020. She was also the general chair for the
>> International Conference on Machine Learning (ICML) 2021, a board member of
>> the International Machine Learning Society, and a co-organizer for the Simons
>> semester on Foundations of Machine Learning.
>>
>> *Host:* Avrim Blum
>>
>>
>>
>>
>> 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, Oct 25, 2024 at 1:35 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When*:    Monday, October 28th from* 11**am - 12pm CT*
>>>
>>> *Where*:   Talk will be given *live, in-person* at
>>>                TTIC, 6045 S. Kenwood Avenue
>>>                5th Floor, *Room 530*
>>>
>>> *Virtually*: *tba*
>>>
>>> *Who:  *    Nina Balcan, Carnegie Mellon University
>>>
>>>
>>> *Title:      *Online learning in Stackelberg Security Games
>>>
>>> *Abstract:* In a Stackelberg Security Game, a defender commits to a
>>> randomized deployment of security resources, and an attacker best responds
>>> by attacking a target that maximizes their utility. While algorithms for
>>> computing an optimal strategy for the defender to commit to have been used
>>> in several real-world applications, deployed applications require knowledge
>>> about the utility function of the potential attacker. In this talk I will
>>> describe an online learning approach for addressing this problem. We
>>> consider algorithms that prescribe a randomized strategy for the defender
>>> at each step against an adversarially chosen sequence of attackers and
>>> obtain feedback on their choices. I will discuss online algorithms whose
>>> regret (when compared to the best fixed strategy in hindsight) is sublinear
>>> in the number of time steps. I will also consider an extension that handles
>>> auxiliary contextual information that is often readily available to each
>>> player (e.g. traffic patterns or weather conditions) and discuss what no
>>> regret guarantees are possible in this even more realistic scenario.
>>>
>>> *Bio: *Maria Florina Balcan is the Cadence Design Systems Professor of
>>> Computer Science in the School of Computer Science at Carnegie Mellon
>>> University. Her main research interests are machine learning, artificial
>>> intelligence, theory of computing, and algorithmic game theory. She is a
>>> Simons Investigator, an ACM Fellow, a Sloan Fellow, a Microsoft Research
>>> New Faculty Fellow, and the recipient of the ACM Grace Murray Hopper Award,
>>> NSF CAREER award, and several best paper awards. She  has given
>>> distinguished lectures and invited keynote talks across different research
>>> fields (including machine learning, information theory, mathematics, and
>>> algorithmic game theory).  She has co-chaired major conferences in the
>>> field: the Conference on Learning Theory (COLT) 2014, the International
>>> Conference on Machine Learning (ICML) 2016, and Neural Information
>>> Processing Systems (NeurIPS) 2020. She was also the general chair for the
>>> International Conference on Machine Learning (ICML) 2021, a board member of
>>> the International Machine Learning Society, and a co-organizer for the Simons
>>> semester on Foundations of Machine Learning.
>>>
>>> *Host:* Avrim Blum
>>>
>>>
>>> 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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