NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:5101
Title:Covariate-Powered Empirical Bayes Estimation

This theory paper provides a number of novel results, including theoretical analysis of minimax bounds and an empirical analysis, for combinations of relatively simple statistical estimators and machine learning models of covariate information. The paper shows that these combinations improve on both the simple estimator alone and the machine learning model alone. The main concern raised by the reviewers is that the paper provides limited empirical validation. I disagree with this assessment, as the paper should be seen as a machine learning theory paper. As the proposed framework includes a number of advanced machine learning models, including XGBoost it should be very relevant for the NeurIPS community. Additionally, it provides a empirical validations of their proposed approach on a simulated data set and on real-world MovieLens ratings.