where X is the parameter space, i.e. a structured input domain that can be explored during optimization. In a BO setting, the objective function is considered to be a black-box function, meaning its structure is a priori unknown, and can only be sequentially resolved by empirical measurement. Black-box functions also do not provide access to gradient information, and measurements are typically expected to be corrupted by noise.