Before revision:  Our approach is agnostic about the properties of the machine learning estimators used to produce proxies, and it completely avoids making any strong assumption.
After revision:  Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. Our approach is agnostic and does not make unrealistic or hard-to-check assumptions; we don't require conditions for consistency of the ML methods.