Reviews: Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent

The other reviewers also convinced me that despite not having the right assumptions for the mention applications, the work might still be useful in other applications. I request the authors to remove the applications mentioned in the introduction or to explicitly write that their assumptions are not satisfied for them. Based on this points, I increase my score from 4 to 6. Let me also clarify on why I believe having the right assumption is important and what I dislike about the theory. SARAH is an interesting method as it does not require bounded gradients and, at the same time, there are settings where the its known complexity is better than that of SGD.