Review for NeurIPS paper: Towards Convergence Rate Analysis of Random Forests for Classification

Weaknesses: - The studied algorithms remain quite far from real random forests (no bootstrap sampling, split choices are fully independent of the data, trees are pruned, etc.) - As in other results in the literature, convergence rates for forests are by-product of convergence rate of individual trees (using Lemma 1). The results therefore do not really show the benefit of using forests instead of trees in terms of convergence rate. This should be discussed in the paper I think. No real conclusion is drawn from the theoretical results that would help better understand standard RF or suggest modification to these methods. I think this kind of very technical contribution would be more appropriate for a journal submission than for a conference (given the limited time allotted for reviewing).