Review for NeurIPS paper: Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions

This paper studies trade-offs between approximation quality and smoothness of "soft"-max functions. The natural exponential function has optimal tradeoff between expected additive approximation and smoothness measured with respect to Renyi divergence but suboptimal when measured via L_p norms. The authors present a new the piecewise linear function which is the optimal one for norms. The reviewers found this to be an well-written and thorough paper on an important problem of broad interest in machine learning. I recommend this paper for acceptance.