Calibrated Uncertainty Estimation Improves Bayesian Optimization. (arXiv:2112.04620v2 [cs.LG] UPDATED)

Bayesian optimization is a sequential procedure for obtaining the global
optimum of black-box functions without knowing a priori their true form. Good
uncertainty estimates over the shape of the objective function are essential in
guiding the optimization process. However, these estimates can be inaccurate if
the true objective function violates assumptions made by its model (e.g.,
Gaussianity). This paper studies which uncertainties are needed in Bayesian
optimization models and argues that ideal uncertainties should be calibrated --
i.e., an 80% …

arxiv bayesian optimization uncertainty