标题： 从作者到审稿人：利用排名来改进同行评审 显示英文标题

标题： From Authors to Reviewers: Leveraging Rankings to Improve Peer Review

摘要： 本文是对Su等人于2025年发表的JASA讨论论文的讨论。我们想祝贺作者对2023 ICML排名数据进行了全面而有见地的实证研究。近年来，由于提交的手稿数量迅速增长，机器学习（ML）会议的评审质量已成为一个重大关注点。在本次讨论中，我们提出了一种替代Su等人（2025）的方法，该方法利用评审者的排名信息而不是作者的排名信息。我们模拟了与2023 ICML会议提交非常相似的评审数据。我们的结果表明，（i）结合评审者的排名信息可以显著提高对每篇论文质量的评估，通常优于仅使用作者的排名信息；（ii）在大多数情况下，结合评审者和作者的排名信息能最准确地评估提交的论文。 显示英文摘要

摘要： This paper is a discussion of the 2025 JASA discussion paper by Su et al. (2025). We would like to congratulate the authors on conducting a comprehensive and insightful empirical investigation of the 2023 ICML ranking data. The review quality of machine learning (ML) conferences has become a big concern in recent years, due to the rapidly growing number of submitted manuscripts. In this discussion, we propose an approach alternative to Su et al. (2025) that leverages ranking information from reviewers rather than authors. We simulate review data that closely mimics the 2023 ICML conference submissions. Our results show that (i) incorporating ranking information from reviewers can significantly improve the evaluation of each paper's quality, often outperforming the use of ranking information from authors alone; and (ii) combining ranking information from both reviewers and authors yields the most accurate evaluation of submitted papers in most scenarios.