Review for NeurIPS paper: Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing

Additional Feedback: I am not particular familiar with existing theory on solving Lipschitz constrained optimization, but the main theory in this paper looks novel. The paper currently looks like unfinished and has some missing details, especially on how the proposed algorithm is applied to MNIST. Also it is worthwhile to study a few other datasets with different complexity, to show their "fundamental lower bound" (line 18). The provably robust training scheme needs to be demonstrated on a few small datasets. I believe this paper has the potential to become a very good paper, but currently it looks incomplete for this conference.