Review for NeurIPS paper: Submodular Meta-Learning

Summary and Contributions: In this paper, the authors describe a meta-learning framework extension to the discrete setting. Specifically, they consider the cases where the functions that the tasks aim to maximize are monotone and submodular set functions. They show that both of these algorithms are at least 1/2-optimal. They also present a meta-greedy algorithm (which chooses the better solution between the previous two algorithms) and prove that it is at least 0.53-optimal. The authors also present a randomized meta-greedy algorithm. Finally, the paper also describes results from applying the above algorithms to two practical problems: ride-sharing and movie-recommendation.