CS Events

Computer Science Department Colloquium Eliciting Information without Verification from Humans and Machines
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Speaker: Yuqing Kong

Bio

Yuqing Kong is currently an assistant professor at the Center on Frontiers of Computing Studies (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.

Location : Core 301

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Event Type: Computer Science Department Colloquium

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).

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Contact  David Pennock

Zoom link
https://rutgers.zoom.us/j/97689252220?pwd=SnVFd0VINzhMcFFFakhDTzFSbXVHQT09
Meeting ID: 976 8925 2220
Password: 408350