We begin the discussion of related work with problem types that are similar in spirit to that of our unknown constraints optimization problem definition, but outside the scope of this work. Safe BO is a closely related topic where candidate parameters must exceed some safety threshold to be recommended for measurement. Here, all points in the optimization campaign must be in the "safe" region, i.e. must never violate the constraint function, corresponding to maximal risk aversion. Safe BO is useful for applications such as optimization of medical treatment while maintaining patient well-being, or robotic control while avoiding catastrophic damage to the system. In this work, we do not explicitly study this case, and assume constraint violations are inconvenient rather than catastrophic. Another related problem type is BO with missing inputs, where missing values in training data are imputed via sampling from a probability distribution.