[Theory] NOW: 7/18 Thesis Defense: Han Shao, TTIC
Mary Marre via Theory
theory at mailman.cs.uchicago.edu
Thu Jul 18 10:56:00 CDT 2024
*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 Thu, Jul 18, 2024 at 9:30 AM 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>*
>
>
> On Wed, Jul 17, 2024 at 1:00 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>*
>>
>>
>> On Mon, Jul 15, 2024 at 6:12 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>*
>>>
>>>
>>> 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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