Review for NeurIPS paper: Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study

Relation to Prior Work: It is clear how this work differs from the prior literature, since it is the first one to study implicit regularization in the setting of stochastic convex optimization. However, the related literature section could be improved. First, implicit regularization dates back to much earlier works than the ones concerning neural networks (the first theoretical work on implicit regularization is due to Buhlmann and Yu 2003 "Boosting with the l2 loss"). Given that the work the authors present has little or nothing to do with neural networks, the related work section should be expanded to include other authors who developed the theory of implicit regularization for gradient descent over the past 20 years. Also, it is worth noting that one of the central reasons motivating the sutudy of early stopping and implicit regularization is the computational efficiency of the method in comparison to the explicit regularization schemes.