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Bayesian optimization (BO) is a powerful sequential optimization approach forseeking the global optimum of black-box functions for sample efficiencypurposes. Evaluations of black-box functions can be expensive, renderingreduced use of labeled data desirable. For the first time, we introduce ateacher-student model, called $\texttt{TSBO}$, to enable semi-supervisedlearning that can make use of large amounts of cheaply generated unlabeled dataunder the context of BO to enhance the generalization of data query models. Ourteacher-student model is uncertainty-aware and offers a practical mechanism forleveraging the pseudo labels generated for unlabeled data while dealing withthe involved risk. We show that the selection of unlabeled data is key to$\texttt{TSBO}$. We optimize unlabeled data sampling by generating unlabeleddata from a dynamically fitted extreme value distribution or a parameterizedsampling distribution learned by minimizing the student feedback.$\texttt{TSBO}$ is capable of operating in a learned latent space with reduceddimensionality, providing scalability to high-dimensional problems.$\texttt{TSBO}$ demonstrates the significant sample efficiency in severalglobal optimization tasks under tight labeled data budgets.

High-dimensional Bayesian Optimization via Semi-supervised Learning with Optimized Unlabeled Data Sampling