Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
All reviewers are positive about the paper. The paper addresses the problem of black-box optimization, currently of wide interest especially for reinforcement learning. The authors propose adaptive active subspaces techniques for black-box optimization. While the theoretical results seem currently limited, the experimental comparison is detailed and extensive. The proposed approach is therefore quite promising. We recommend to take the reviewers' comments and suggestions into account while preparing the camera ready final version of the paper. Accept.