Review for NeurIPS paper: Margins are Insufficient for Explaining Gradient Boosting

R2 support rejects by mentioning that the results do not directly take into account some specificities of the gradient boosting (GB) learning algorithms in particular the problems of normalization of the regressors that have to be combined. That being said, the theory presented in the paper is fairly general, giving new insights on (gradient) boosting methods, it provides progress on margin bounds in both direction (lower upper bounds) with respect to current state of the art. The wide use of (gradient) boosting methods make the paper interesting for the community. Based on these positive points, I recommend acceptance. However, the authors should consider revising their paper according to the following points: -The theory provided is rather general, not specific to GB, and must presented accordingly.