Reviews: Scalable Global Optimization via Local Bayesian Optimization

Major * I found this paper to be very exciting, presenting a promising methodology addressing some of the most critical bottlenecks of Bayesian Optimization, with a focus on large data sets (being therefore relevant for high-dimensional BO as well, where sample sizes typically need to be substantially increased with the dimension). So, one is far from filling the space, right? Not using these for some good reason is one thing, but putting it the way it is put here sounds like it is not possible to go batch-sequential with EI... * In the main contributions presented throughout Section 3, two main ideas are confounded here: splitting the data so as to obtain local models AND using TS as infill criterion. Which is (most) responsible for improved performances over the state of the art? Minor (selected points) * Page 1: What does "outputscales" mean?