Bayesian Optimization (BO) has proven to be very successful at optimizing a static, noisy, costly-to-evaluate black-box function \(f : \mathcal{S} \to \mathbb{R}\). However, optimizing a black-box which is also a function of time (i.e., a dynamic function) \(f : \mathcal{S} \times \mathcal{T} \to \mathbb{R}\) remains a challenge, since a dynamic Ba...

This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization