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Speaker: Yichi Zhang (University of Michigan)
Talk Title: Eliciting Honest Information From Authors Using Sequential Review
Abstract: In the setting of conference peer review, the conference aims to accept high-quality papers and reject low-quality papers based on noisy review scores. A recent work proposes the isotonic mechanism, which can elicit the ranking of paper qualities from an author with multiple submissions to help improve the conference’s decisions. However, the isotonic mechanism relies on the assumption that the author’s utility is both an increasing and a convex function with respect to the review score, which is often violated in peer review settings (e.g. when authors aim to maximize the number of accepted papers). In this paper, we propose a sequential review mechanism that can truthfully elicit the ranking information from authors while only assuming the agent’s utility is increasing with respect to the true quality of her accepted papers. The key idea is to review the papers of an author in a sequence based on the provided ranking and conditioning the review of the next paper on the review scores of the previous papers. Advantages of the sequential review mechanism include 1) eliciting truthful ranking information in a more realistic setting than prior work; 2) improving the quality of accepted papers, reducing the reviewing workload and increasing the average quality of papers being reviewed; 3) incentivizing authors to write fewer papers of higher quality.
Bio: Yichi Zhang is a Ph.D. candidate at the School of Information, University of Michigan, advised by Prof. Grant Schoenebeck. His research interests lie in the intersection of computer science and economics, exploring areas such as information elicitation, crowdsourcing, peer grading, peer review, and their interactions with machine learning and AI. He aims to design theoretically strong and practical mechanisms to motivate effort and incentivize honest behaviors across various applications such as crowdsourcing, conference peer review, and recommender systems. His work has been featured in esteemed peer-reviewed conferences, including EC, WWW, and AAAI.