Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in practice that the best-performing AF in terms of regret can vary significantly under different types of black-box functions. It has remained a challenge to design one AF that can attain the best performance over a wide variety of black-box functions.

Yet, despite recent progress, CO problems still pose a significant challenge to neural networks. Successful models often rely on supervision, either in the form of labeled instances [45, 62, 35] or of expert demonstrations [27]. This success comes with drawbacks: obtaining labels for hard problem instances can be computationally infeasible [86],and direct supervision can lead topoor generalization[36].