Reviews: Bootstrap Model Aggregation for Distributed Statistical Learning

This is a successive work correcting previous research on using KL averaging combining subset estimators. I think the most appealing point for using KL averaging, despite the computational issue, is its power in dealing with latent variable models. There is another line of work in using geometric median to combine subset estimators the authors might want to compare to, for example, Minsker (2013) and Hsu and Sabato (2013). These algorithms are simple and efficient in most cases, but might not be doing well for latent variable models. The variance reduction technique used in this article is very similar to the de-bias technique used in Javanmard and Montanari (2015) and Lee et a. (2015), so the theoretical contribution is kind of limited.