[Theory] REMINDER: 1/25 TTIC Colloquium: Yuqing Kong, Peking University

[Mary Marre]

*When:*        Thursday, January 25, 2024 at* 11:00** a**m 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=f98936a2-2f72-4005-b55f-b0fd001649be>*
)


*Who: *         Yuqing Kong, Peking University

------------------------------

*Title: *Eliciting Information without Verification from Humans and
Machines

*Abstract:* Many application domains rely on eliciting high-quality
(subjective) information. This presentation will talk about how to elicit
and aggregate information from both human and machine participants,
especially when the information cannot be directly verified. The first part
of the talk presents a mechanism, DMI-Mechanism, designed to incentivize
truth-telling in the setting where participants are assigned multiple
multi-choice questions (e.g. what’s the quality of the above content?
High/Low). DMI-Mechanism ensures that truthful responses are more rewarding
than any less informative strategy. The implementation of DMI-Mechanism is
straightforward, requiring no verification or prior knowledge, and involves
only two participants and four questions for binary-choice scenarios. When
applied to machine learning, DMI-Mechanism results in a loss function that
is invariant to label noise. The second part of the talk discusses the
elicitation of information not just from humans but also from machines.
Recognizing the limitations in time and resources that humans and machines
have, the talk introduces a method to elicit and analyze the 'thinking
hierarchy' of both entities. This approach not only facilitates the
aggregation of information when the majority of agents are at less
sophisticated 'thinking' levels but also provides a unique way to compare
humans and machines.

This talk is based a series of works including Kong (SODA 2020, ITCS 2022,
JACM 2024), Xu, Cao, Kong, Wang (NeurIPS 2019), Kong, Li, Zhang, Huang, Wu
(NeurIPS 2022), Huang, Kong, Mei (2024).

*Bio: *Yuqing Kong is currently an assistant professor at The Center of
Frontier Computing Science (CFCS), Peking University. She obtained her
Ph.D. degree from the Computer Science and Engineering Department at
University of Michigan in 2018 and her bachelor degree in mathematics from
University of Science and Technology of China in 2013. Her research
interests lie in the intersection of theoretical computer science and the
areas of economics: information elicitation, prediction markets, mechanism
design, and the future applications of these areas to crowdsourcing and
machine learning.
*Host:* *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 Fri, Jan 19, 2024 at 7:27 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Thursday, January 25, 2024 at* 11:00** a**m 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=f98936a2-2f72-4005-b55f-b0fd001649be>*
> )
>
>
> *Who: *         Yuqing Kong, Peking University
>
> ------------------------------
>
> *Title: *Eliciting Information without Verification from Humans and
> Machines
>
> *Abstract:* Many application domains rely on eliciting high-quality
> (subjective) information. This presentation will talk about how to elicit
> and aggregate information from both human and machine participants,
> especially when the information cannot be directly verified. The first part
> of the talk presents a mechanism, DMI-Mechanism, designed to incentivize
> truth-telling in the setting where participants are assigned multiple
> multi-choice questions (e.g. what’s the quality of the above content?
> High/Low). DMI-Mechanism ensures that truthful responses are more rewarding
> than any less informative strategy. The implementation of DMI-Mechanism is
> straightforward, requiring no verification or prior knowledge, and involves
> only two participants and four questions for binary-choice scenarios. When
> applied to machine learning, DMI-Mechanism results in a loss function that
> is invariant to label noise. The second part of the talk discusses the
> elicitation of information not just from humans but also from machines.
> Recognizing the limitations in time and resources that humans and machines
> have, the talk introduces a method to elicit and analyze the 'thinking
> hierarchy' of both entities. This approach not only facilitates the
> aggregation of information when the majority of agents are at less
> sophisticated 'thinking' levels but also provides a unique way to compare
> humans and machines.
>
> This talk is based a series of works including Kong (SODA 2020, ITCS 2022,
> JACM 2024), Xu, Cao, Kong, Wang (NeurIPS 2019), Kong, Li, Zhang, Huang, Wu
> (NeurIPS 2022), Huang, Kong, Mei (2024).
>
> *Bio: *Yuqing Kong is currently an assistant professor at The Center of
> Frontier Computing Science (CFCS), Peking University. She obtained her
> Ph.D. degree from the Computer Science and Engineering Department at
> University of Michigan in 2018 and her bachelor degree in mathematics from
> University of Science and Technology of China in 2013. Her research
> interests lie in the intersection of theoretical computer science and the
> areas of economics: information elicitation, prediction markets, mechanism
> design, and the future applications of these areas to crowdsourcing and
> machine learning.
> *Host:* *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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