Practical Path-based Bayesian Optimization
Jose Pablo Folch James Odgers Shiqiang Zhang Robert M Lee Behrang Shafei David Walz Calvin Tsay Mark van der Wilk Ruth Misener
There has been a surge in interest in data-driven experimental design with
applications to chemical engineering and drug manufacturing. Bayesian
optimization (BO) has proven to be adaptable to such cases, since we can model
the reactions of interest as expensive black-box functions. Sometimes, the cost
of this black-box functions can be separated into two parts: (a) the cost of
the experiment itself, and (b) the cost of changing the input parameters. In
this short paper, we extend the SnAKe algorithm to deal with both types of
costs simultaneously. We further propose extensions to the case of a maximum
allowable input change, as well as to the multi-objective setting.