On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair.

In linear regression, interpolating the square loss is equivalent to interpolating many other losses (such as the absolute loss) on the training set. Similarly, in the context of linear classification, many works (Soudry et al. 2018; Ji and Telgarsky 2019; Muthukumar et al. 2021) have shown that optimizing

LetT be the time horizon andPT be the path-length that essentially reflects the non-stationarity of environments, the state-of-the-art dynamicregretis O( p T(1+PT)).