  A successful peer review process requires that qualified and interested
reviewers are assigned to each paper. Most automated reviewer assignment
approaches estimate a real-valued affinity score for each paper-reviewer pair
that acts as a proxy for the quality of the match, and then assign reviewers to
maximize the sum of affinity scores. Most affinity score estimation methods are
inherently noisy: reviewers can only bid on a small number of papers, and
textual similarity models and subject-area matching are inherently noisy
estimators. Current paper assignment systems are not designed to rigorously
handle noise in the peer-review matching market. In this work, we assume
paper-reviewer affinity scores are located in or near a high-probability region
called an uncertainty set. We maximize the worst-case sum of scores for a
reviewer assignment over the uncertainty set. We demonstrate how to robustly
maximize the sum of scores across various classes of uncertainty sets, avoiding
potentially serious mistakes in assignment. Our general approach can be used to
integrate a large variety of paper-reviewer affinity models into reviewer
assignment, opening the door to a much more robust peer review process.
