Review for NeurIPS paper: Bayesian Robust Optimization for Imitation Learning

Clarity: Overall, I think the paper is fairly well written. I understand that the authors are working within the page restrictions of the conference. With that said, I think there is substantial room for improvement in the paper presentation. First, I think there are more specific ways to describe the contributions (copied from summary): 1) a linear programming formulation to compute the optimal policy for CVaR; 2) show how to use this to implement robust policy optimization under a prior and robust imitation learning; 3) demonstrate favorable comparisons with existing risk-sensitive and risk neutral algorithms for both settings. Right now I think that the description of the contributions hides the most useful contribution.