Review for NeurIPS paper: In search of robust measures of generalization

This paper evaluates various "generalization measures" --- numbers computed from training data and training algorithm and network properties --- in terms of their success predicting generalization. The work builds on the prior work of Jiang et al. (their [6]) in ways they clearly define and thus they provide a new set of results on similar questions. Their changes are interesting, and since generalization of deep networks is of such extensive interest to so many, I also feel these results will be valuable. I look forward to seeing this paper appear, and support the authors on future work. Since IMO figure 1 is the main core of this paper, I think it would be reasonable to spend more time explaining figure 1 and even expanding it, in the process shortening some other stuff and moving to appendices?