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Workshop: Adaptive Experimental Design and Active Learning in the Real World

Practical Path-based Bayesian Optimization

Jose Pablo Folch · James Odgers · Shiqiang Zhang · Robert 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.

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