with mean ŷ ∈ R and variance σ 2 ∈ R. The surrogate model reflects our current beliefs about f (x) and serves two purposes in BO. First, to guide exploitation, ŷ (x) cheaply estimates the properties of the remaining, unevaluated NPMs, i.e., ŷ (x) is a cheap-to-evaluate approximation of the expensive-to-evaluate objective function f (x). Second, to guide exploration, σ 2 (x) quantifies the uncertainties in the predicted properties of the unevaluated NPMs. This makes us aware of "blind spots" of the surrogate model-regions in the NPM feature space we need to explore to improve our approximation ŷ (x) and reduce the uncertainty in our beliefs about f (x).