Denzler S, Aigner KM, Lüer L, Brabec C, Liers F (2025)
Publication Status: Submitted
Publication Type: Unpublished / Preprint
Future Publication Type: Journal article
Publication year: 2025
URI: https://opus4.kobv.de/opus4-trr154/frontdoor/index/index/docId/606
Open Access Link: https://opus4.kobv.de/opus4-trr154/frontdoor/index/index/docId/606
We propose a novel online learning framework for robust Bayesian optimization of uncertain black-box functions. While Bayesian optimization is well-suited for data-efficient optimization of expensive objectives, its standard form can be sensitive to hidden or varying parameters. To address this issue, we consider a min–max robust counterpart of the optimization problem and develop a practically efficient solution algorithm, BROVER (Bayesian Robust Optimization via Exploration with Regret minimization). Our method combines Gaussian process regression with a decomposition approach: the minimax structure is split into a non-convex online learner based on the Follow-the-Perturbed-Leader algorithm together with a subsequent minimization step in the decision variables. We prove that the theoretical regret bound converges under mild assumptions, ensuring asymptotic convergence to robust solutions. Numerical experiments on synthetic data validate the regret guarantees and demonstrate fast convergence to the robust optimum. Furthermore, we apply our method to the robust optimization of organic solar cell performance, where hidden process parameters and experimental variability naturally induce uncertainty. Our results on real-world datae show that BROVER identifies solutions with strong robustness properties within relatively few iterations, thereby offering a modern and practical approach for data-driven black-box optimization under uncertainty.
APA:
Denzler, S., Aigner, K.-M., Lüer, L., Brabec, C., & Liers, F. (2025). Robust Bayesian Optimization with an Application to Material Science. (Unpublished, Submitted).
MLA:
Denzler, Sebastian, et al. Robust Bayesian Optimization with an Application to Material Science. Unpublished, Submitted. 2025.
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