[Theory] REMINDER: 7/18 Thesis Defense: Han Shao, TTIC

[Mary Marre via Theory]

*When*:   Thursday, July 18th from *11am **- 1pm CT*

*Where*:  Talk will be given *live, in-person* at
              TTIC, 6045 S. Kenwood Avenue
              5th Floor, *Room 529*

*Virtually*: via *Zoom*
<https://uchicago.zoom.us/j/97780917952?pwd=UTk339P6epCeDf1K23mqC5UY7b0wvy.1>


*Who:  *   Han Shao, TTIC



*Title: *   Trustworthy Machine Learning under Social and Adversarial Data
Sources
*Abstract:* Machine learning has witnessed remarkable breakthroughs in
recent years. Many machine learning techniques assume that the training and
test data are sampled from an underlying distribution and aim to find a
predictor with low population loss. However, in the real world, data may be
generated by *strategic individuals*, collected by *self-interested data
collectors*, possibly poisoned by *adversarial attackers*, and used to
create predictors, models, and policies satisfying *multiple objectives*.
As a result, predictors may underperform. To ensure the success of machine
learning, it is crucial to develop trustworthy algorithms capable of
handling these factors.


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

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, Jul 10, 2024 at 4:55 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When*:   Thursday, July 18th from *11am **- 1pm CT*
>
> *Where*:  Talk will be given *live, in-person* at
>               TTIC, 6045 S. Kenwood Avenue
>               5th Floor, *Room 529*
>
> *Virtually*: via *Zoom*
> <https://uchicago.zoom.us/j/97780917952?pwd=UTk339P6epCeDf1K23mqC5UY7b0wvy.1>
>
>
> *Who:  *   Han Shao, TTIC
>
>
>
> *Title: *   Trustworthy Machine Learning under Social and Adversarial
> Data Sources
> *Abstract:* Machine learning has witnessed remarkable breakthroughs in
> recent years. Many machine learning techniques assume that the training and
> test data are sampled from an underlying distribution and aim to find a
> predictor with low population loss. However, in the real world, data may be
> generated by *strategic individuals*, collected by *self-interested data
> collectors*, possibly poisoned by *adversarial attackers*, and used to
> create predictors, models, and policies satisfying *multiple objectives*.
> As a result, predictors may underperform. To ensure the success of machine
> learning, it is crucial to develop trustworthy algorithms capable of
> handling these factors.
>
>
> *Advisor: Avrim Blum <avrim at ttic.edu>*
>
>
> 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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