Efficient computation of the Knowledge Gradient for Bayesian Optimization. (arXiv:2209.15367v1 [cs.LG])

Bayesian optimization is a powerful collection of methods for optimizing
stochastic expensive black box functions. One key component of a Bayesian
optimization algorithm is the acquisition function that determines which
solution should be evaluated in every iteration. A popular and very effective
choice is the Knowledge Gradient acquisition function, however there is no
analytical way to compute it. Several different implementations make different
approximations. In this paper, we review and compare the spectrum of Knowledge
Gradient implementations and propose One-shot …

arxiv bayesian computation gradient knowledge optimization