@inproceedings{
shao2023preferenceguided,
title={Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine},
author={Ketong Shao and Diego Romeres and Ankush Chakrabarty and Ali Mesbah},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=utVPf9dRgy}
}
This paper investigates the adaptation of control policies for personalized dose delivery in plasma medicine using preference-learning based Bayesian optimization. Preference learning empowers users to incorporate their preferences or domain expertise during the exploration of optimal control policies, which often results in fast attainment of personalized treatment outcomes. We establish that, compared to multi-objective Bayesian optimization (BO), preference-guided BO offers statistically faster convergence and computes solutions that better reflect user preferences. Moreover, it enables users to actively provide feedback during the policy search procedure, which helps to focus the search in sub-regions of the search space likely to contain preferred local optima. Our findings highlight the suitability of preference-learning-based BO for adapting control policies in plasma treatments, where both user preferences and swift convergence are of paramount importance.
Preference-Guided Bayesian Optimization for Control Policy Learning: Application to Personalized Plasma Medicine
Ketong Shao Diego Romeres Ankush Chakrabarty Ali Mesbah