Bridging logical reasoning and deep learning is crucial for advanced AI systems.

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.

Our algorithm employsonecolumn forsubgradient descent ineach iteration, whereas thedual project subgradient algorithm requires the whole constraint matrix and conducts matrix multiplication in each iteration. In addition, a class of backpressure/max-weight algorithms [25] are developed in the control/queueing literature and the backpressure algorithm can be interpreted from a view of pressuregradient.

Work done while working at Allen Institute for AI.