Reviews: Multi-Information Source Optimization

This paper deals with the important topic of optimization in cases where in addition to costly evaluations of the objective function, it is possible to evaluate cheaper approximations of it. This framework is referred to as MISO (multi-information source optimization) in the paper, where Bayesian optimization strategies relying on Gaussian process models are considered. An original MISO approach is presented, misoKG, that relies on an adaption of the Knowledge Gradient algorithm in multi-information source optimization settings. The method is shown to achieve very good results and to outperform considered competitors on three test cases. Overall, I am fond of the ideas and the research directions tackled by the paper, and I found the contributions principled and practically relevant.