Review for NeurIPS paper: Finding the Homology of Decision Boundaries with Active Learning

The authors presents an application of active learning to the problem of finding the homology of classifier/dataset decision boundaries. The method shows benefits empirically for learning a homology and is also paired with upper bound guarantees on the required number of labels. There was a concern raised around the correctness of the label complexity proof, which the authors have suggested a fix for in the rebuttal. This has satisfied the reviewer, however, there is an additional gap between theory and the algorithm implementation pointed out. This was not deemed a critical flaw, but please do discuss it in the final version.