Review for NeurIPS paper: Estimating weighted areas under the ROC curve

One contribution seems to have been in defining a surrogate functional g (line 166) that replaces the \mu(0) term in a denominator term with an arbitrary parameter c and then using a uniform convergence bound over values of c to ensure that estimation does take place even if c is replaced with its actual value of \mu(0). Another contribution seems to be in fine tuning the proof technique used to prove Proposition 5. The main contribution is a proof for obtaining generalization bound for weighted areas under the ROC curve for Lipschitz weight functions.