Harvard University

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint func-tions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general frame-work to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allo-cation subject to topic sparsity constraints, tun-ing a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence di-agnostics.