Smooth convex optimization problems over polytopes are an important class of problems that appear in many settings, such as low-rank matrix completion [1],structured supervised learning [2,3],electrical flowsovergraphs [4],video co-localization in computer vision [5], traffic assignment problems [6], and submodular function minimization [7].

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.

To solve this problem, convergent algorithms are developed with both single-loop and stochastic variants. Notably, this is the firstsingle-loop primalalgorithmforconstrained optimization toourknowledge.

Meta learning is a promising paradigm to enable skill transfer across tasks.

Meta learning is a promising paradigm to enable skill transfer across tasks.