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In 2023, the International Conference on Machine Learning (ICML) requiredauthors with multiple submissions to rank their submissions based on perceivedquality. In this paper, we aim to employ these author-specified rankings toenhance peer review in machine learning and artificial intelligence conferencesby extending the Isotonic Mechanism to exponential family distributions. Thismechanism generates adjusted scores that closely align with the original scoreswhile adhering to author-specified rankings. Despite its applicability to abroad spectrum of exponential family distributions, implementing this mechanismdoes not require knowledge of the specific distribution form. We demonstratethat an author is incentivized to provide accurate rankings when her utilitytakes the form of a convex additive function of the adjusted review scores. Fora certain subclass of exponential family distributions, we prove that theauthor reports truthfully only if the question involves only pairwisecomparisons between her submissions, thus indicating the optimality of rankingin truthful information elicitation. Moreover, we show that the adjusted scoresimprove dramatically the estimation accuracy compared to the original scoresand achieve nearly minimax optimality when the ground-truth scores have boundedtotal variation. We conclude the paper by presenting experiments conducted onthe ICML 2023 ranking data, which show significant estimation gain using theIsotonic Mechanism.

The Isotonic Mechanism for Exponential Family Estimation