Entropy Minimization for Optimization of Expensive, Unimodal Functions
Optimization and Control
2023-02-23 v1
Abstract
Maximization of an expensive, unimodal function under random observations has been an important problem in hyperparameter tuning. It features expensive function evaluations (which means small budgets) and a high level of noise. We develop an algorithm based on entropy reduction of a probabilistic belief about the optimum. The algorithm provides an efficient way of estimating the computationally intractable surrogate objective in the general Entropy Search algorithm by leveraging a sampled belief model and designing a metric that measures the information value of any search point.
Cite
@article{arxiv.2302.11386,
title = {Entropy Minimization for Optimization of Expensive, Unimodal Functions},
author = {Xiaohe Luo and Warren B. Powell},
journal= {arXiv preprint arXiv:2302.11386},
year = {2023}
}