Review for NeurIPS paper: Stochastic Optimization with Laggard Data Pipelines

The paper is a theoretical analysis of the behaviour of "echoed gradients" in convex optimization. The investigation is timely, and will cast light on an interesting area of current practice. More than one reviewer believes the paper should explicitly handle the non-convex case. I disagree, and side with the authors that the convex case is sufficient. The relevant non-convex optimizers generally contain convex stepping as a subprogram, so this analysis is reasonable.