Review for NeurIPS paper: Spike and slab variational Bayes for high dimensional logistic regression

Additional Feedback: Restricted to the studied problem, I would love to see more comments on the advantage of VB over frequentist approaches using, say, penalized MLE. It is my understanding that the main advantage of VB is not on estimation/prediction but on inference (e.g., establishing confidence intervals)? If so, would establishing validity of the confidence interval derived by VB (i.e., Bernstein-von Mises type results) be more interesting? They are exceedingly clear to me, and combined with the other referees' comments on novelty, made me to accordingly raise my score further. Speaking about Bernstein-von Mises type results, in case the authors missed it, V. Spokoiny had some very exciting progresses to extend them to high dimensions in a general M-estimation framework; cf.