Reviews: Estimating the class prior and posterior from noisy positives and unlabeled data

I note that the first method in particular also does not require the use of calibrated probabilities, but rather a probabilistically consistent ranker (as the estimate is based on a derivative of the RHS of the ROC curve). Overall, I think the paper's contribution is reasonable, being an extension of (Jain et al., 2016) to the noisy PU case, but I think the novelty is a weak point. Other comments: - section 3 is said to include "a few missing results needed for our approach" -- explicitly identifying which these are seems prudent.