Review for NeurIPS paper: Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition

Additional Feedback: This paper analyzes "double descent" phenomenon, which is when the generalization error of a model peaks at the interpolation threshold (as a function either of model complexity or of sample size). The authors develop a fine-grained bias-variance decomposition which decomposes the risk into the bias and several different variance terms. They apply this decomposition to the random features regression model and show which of these terms lead to divergence. This paper addresses an important issue that has lately been focus of much research. It suggests "fine-grained" bias-variance decomposition that allows to clarify several subtle effects.