[Theory] NOW: 7/29 Thesis Defense: Kevin Stangl, TTIC

[Mary Marre via Theory]

*When*:   Monday, July 29th from 11:30am - 12:30pm CT

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

*Virtually*: via *Zoom*
<https://us02web.zoom.us/j/82622819147?pwd=ubre2fCUgmj4kuOX20j45v6IwyioJU.1>


*Who:  *   Kevin Stangl, TTIC



*Title: * Fairness, Accuracy, and Unreliable Data
*Abstract:* A theme throughout my thesis is thinking about ways and
responses to how a `plain' empirical risk minimization algorithm will be
misleading or ineffective because of a train-test distribution mis-match
due to biased data, strategic behavior, or adversarial data corruptions.
The overarching research goal for these related topics is to provide a
crisp mathematical model for each learning scenario that  exposes different
failure modes and makes trade-offs explicit.

In my defense, I will survey all of my completed research and dive deeply
into two papers, which study a fundamental question in fairness in machine
learning, how effectively or ineffectively a range of fairness constraints
recover from biased and adversarial corruptions in training data.

*Committee: *Avrim Blum (chair), Madhur Tulsiani, Ali Vakilian, and Juba
Ziani (Georgia Tech)


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, Jul 29, 2024 at 10:58 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When*:   Monday, July 29th from 11:30am - 12:30pm CT
>
> *Where*:  Talk will be given *live, in-person* at
>               TTIC, 6045 S. Kenwood Avenue
>               5th Floor, *Room 530*
>
> *Virtually*: via *Zoom*
> <https://us02web.zoom.us/j/82622819147?pwd=ubre2fCUgmj4kuOX20j45v6IwyioJU.1>
>
>
> *Who:  *   Kevin Stangl, TTIC
>
>
>
> *Title: * Fairness, Accuracy, and Unreliable Data
> *Abstract:* A theme throughout my thesis is thinking about ways and
> responses to how a `plain' empirical risk minimization algorithm will be
> misleading or ineffective because of a train-test distribution mis-match
> due to biased data, strategic behavior, or adversarial data corruptions.
> The overarching research goal for these related topics is to provide a
> crisp mathematical model for each learning scenario that  exposes different
> failure modes and makes trade-offs explicit.
>
> In my defense, I will survey all of my completed research and dive deeply
> into two papers, which study a fundamental question in fairness in machine
> learning, how effectively or ineffectively a range of fairness constraints
> recover from biased and adversarial corruptions in training data.
>
> *Committee: *Avrim Blum (chair), Madhur Tulsiani, Ali Vakilian, and Juba
> Ziani (Georgia Tech)
>
> 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, Jul 28, 2024 at 4:08 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When*:   Monday, July 29th from 11:30am - 12:30pm CT
>>
>> *Where*:  Talk will be given *live, in-person* at
>>               TTIC, 6045 S. Kenwood Avenue
>>               5th Floor, *Room 530*
>>
>> *Virtually*: via *Zoom*
>> <https://us02web.zoom.us/j/82622819147?pwd=ubre2fCUgmj4kuOX20j45v6IwyioJU.1>
>>
>>
>> *Who:  *   Kevin Stangl, TTIC
>>
>>
>>
>> *Title: * Fairness, Accuracy, and Unreliable Data
>> *Abstract:* A theme throughout my thesis is thinking about ways and
>> responses to how a `plain' empirical risk minimization algorithm will be
>> misleading or ineffective because of a train-test distribution mis-match
>> due to biased data, strategic behavior, or adversarial data corruptions.
>> The overarching research goal for these related topics is to provide a
>> crisp mathematical model for each learning scenario that  exposes different
>> failure modes and makes trade-offs explicit.
>>
>> In my defense, I will survey all of my completed research and dive deeply
>> into two papers, which study a fundamental question in fairness in machine
>> learning, how effectively or ineffectively a range of fairness constraints
>> recover from biased and adversarial corruptions in training data.
>>
>> *Committee: *Avrim Blum (chair), Madhur Tulsiani, Ali Vakilian, and Juba
>> Ziani (Georgia Tech)
>>
>>
>>
>> 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 Thu, Jul 25, 2024 at 12:42 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When*:   Monday, July 29th from 11:30am - 12:30pm CT
>>>
>>> *Where*:  Talk will be given *live, in-person* at
>>>               TTIC, 6045 S. Kenwood Avenue
>>>               5th Floor, *Room 530*
>>>
>>> *Virtually*: via *Zoom*
>>> <https://us02web.zoom.us/j/82622819147?pwd=ubre2fCUgmj4kuOX20j45v6IwyioJU.1>
>>>
>>>
>>> *Who:  *   Kevin Stangl, TTIC
>>>
>>>
>>>
>>> *Title: * Fairness, Accuracy, and Unreliable Data
>>> *Abstract:* A theme throughout my thesis is thinking about ways and
>>> responses to how a `plain' empirical risk minimization algorithm will be
>>> misleading or ineffective because of a train-test distribution mis-match
>>> due to biased data, strategic behavior, or adversarial data corruptions.
>>> The overarching research goal for these related topics is to provide a
>>> crisp mathematical model for each learning scenario that  exposes different
>>> failure modes and makes trade-offs explicit.
>>>
>>> In my defense, I will survey all of my completed research and dive
>>> deeply into two papers, which study a fundamental question in fairness in
>>> machine learning, how effectively or ineffectively a range of fairness
>>> constraints recover from biased and adversarial corruptions in training
>>> data.
>>>
>>> *Committee: *Avrim Blum (chair), Madhur Tulsiani, Ali Vakilian, and
>>> Juba Ziani (Georgia Tech)
>>>
>>>
>>>
>>> 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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