Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review
This addresses peer review quality in ML/AI conferences, offering a method to leverage author rankings for more accurate score estimation, though it appears incremental as an extension of the Isotonic Mechanism to a broader distribution class.
The paper tackles the problem of enhancing peer review in ML/AI conferences by using author-specified rankings to adjust review scores via the Isotonic Mechanism for exponential family distributions, showing that it improves estimation accuracy dramatically and achieves nearly minimax optimality with bounded total variation.
In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we aim to employ these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the Isotonic Mechanism to exponential family distributions. This mechanism generates adjusted scores that closely align with the original scores while adhering to author-specified rankings. Despite its applicability to a broad spectrum of exponential family distributions, implementing this mechanism does not require knowledge of the specific distribution form. We demonstrate that an author is incentivized to provide accurate rankings when her utility takes the form of a convex additive function of the adjusted review scores. For a certain subclass of exponential family distributions, we prove that the author reports truthfully only if the question involves only pairwise comparisons between her submissions, thus indicating the optimality of ranking in truthful information elicitation. Moreover, we show that the adjusted scores improve dramatically the estimation accuracy compared to the original scores and achieve nearly minimax optimality when the ground-truth scores have bounded total variation. We conclude with a numerical analysis of the ICML 2023 ranking data, showing substantial estimation gains in approximating a proxy ground-truth quality of the papers using the Isotonic Mechanism.