Review for NeurIPS paper: The Power of Comparisons for Actively Learning Linear Classifiers

Clarity: While I can understand the statements in this paper, I think the presentation can be much improved. For example: - As RPU learning implies PAC learning, is there really a need to present Algorithm 1 and Theorem 3.5? Aren't we already happy with Theorem 4.11? - in Algorithm 1, Threshold(S) is only informally defined, and an elaboration is needed. I think basically, the algorithm can successfully approximately recover b if it can find two neighboring and - examples? Also, what is the active learning algorithm used here?