Boundary Exploration for Bayesian Optimization With Unknown Physical
  Constraints
Yunsheng Tian Ane Zuniga Xinwei Zhang Johannes P. Dürholt Payel Das Jie Chen Wojciech Matusik Mina Konaković Luković
Bayesian optimization has been successfully applied to optimize black-box
functions where the number of evaluations is severely limited. However, in many
real-world applications, it is hard or impossible to know in advance which
designs are feasible due to some physical or system limitations. These issues
lead to an even more challenging problem of optimizing an unknown function with
unknown constraints. In this paper, we observe that in such scenarios optimal
solution typically lies on the boundary between feasible and infeasible regions
of the design space, making it considerably more difficult than that with
interior optima. Inspired by this observation, we propose BE-CBO, a new
Bayesian optimization method that efficiently explores the boundary between
feasible and infeasible designs. To identify the boundary, we learn the
constraints with an ensemble of neural networks that outperform the standard
Gaussian Processes for capturing complex boundaries. Our method demonstrates
superior performance against state-of-the-art methods through comprehensive
experiments on synthetic and real-world benchmarks.