Review for NeurIPS paper: Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

Summary and Contributions: This paper aims to improve the reviewer-paper matching algorithms that many computer science conferences use to assign reviewers to submitted papers. Most conferences currently employ a deterministic algorithm with a linear program at its core that maximizes the total match quality (sum of similarity scores) subject to load balancing constraints ensuring that no reviewer is assigned too many papers and every paper is assigned enough reviewers. A problem with a deterministic algorithm is that unethical reviewers can manipulate their similarity scores (either through bids or submitted features) in order to try to get assigned one particular paper in order to boost it or nuke it. Another problem with a deterministic algorithm is that it cannot be shared to the public without the public being able to reverse engineer the match and reveal the reviewers assigned to a paper. The authors show that both problems can be alleviated by going with a randomized algorithm.