Practical Path-Based Bayesian Optimization
Abstract
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.
Cite
Text
Folch et al. "Practical Path-Based Bayesian Optimization." NeurIPS 2023 Workshops: ReALML, 2023.Markdown
[Folch et al. "Practical Path-Based Bayesian Optimization." NeurIPS 2023 Workshops: ReALML, 2023.](https://mlanthology.org/neuripsw/2023/folch2023neuripsw-practical/)BibTeX
@inproceedings{folch2023neuripsw-practical,
title = {{Practical Path-Based Bayesian Optimization}},
author = {Folch, Jose Pablo and Odgers, James A C and Zhang, Shiqiang and Lee, Robert Matthew and Shafei, Behrang and Walz, David and Tsay, Calvin and van der Wilk, Mark and Misener, Ruth},
booktitle = {NeurIPS 2023 Workshops: ReALML},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/folch2023neuripsw-practical/}
}