Review for NeurIPS paper: A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints

Summary and Contributions: The paper considers the problem of maximizing a general monotone DR-submodular function subject to a general convex constraint (general up to some natural assumptions) in the online regret-minimization setting. The paper presents two algorithms for this problem, and proves bounds on their regret (with respect to the 1-1/e offline approximation) as well as the extent to which they violate the constraint on average. In that respect, the paper considers three kinds of regrets: - The traditional adversarial static regret in which the input is selected by an adversary and the algorithm competes with the best single solution in hindsight. For some of these benchmarks there are previous results for the special case in which the constraints are linear. The current paper improves over them both in terms of the generality of the constraint, and in terms of the quality of the guarantees.