\section{Conclusion}
Bayesian optimization with unknown constraints poses challenges in the adaptive tradeoff between optimizing the unknown objective and learning the constraints.
We introduce \algname, which is backed by rigorous theoretical guarantees, to efficiently address constrained Bayesian optimization.
Our key insights include: (1) the ROIs determined through adaptive level-set estimation can congregate and contribute to the overall Bayesian optimization task; 
(2) acquisition functions based on independent GPs can be unified in a principled way. 
Through extensive experiments, we validate the efficacy and robustness of our proposed method across various tasks finding the interior optimum.

