Practical Bayesian optimization for variable cost objectives

Journal article

We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations. Further, we use a novel approach to sampling support points, allowing faster construction of the acquisition function. This allows us to achieve optimization with lower overheads than previous approaches and is implemented for a more general class of problem. We show this approach to be effective on synthetic and real world benchmark problems.

Authors: McLeod, M; Osborne, MA; Roberts, SJ

• Publication status: Not published
• Peer review status: Not peer reviewed
• Publisher: Cornell University
• Journal: arXiv
• Publication date: 2017-03-13