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MF-SCBO represents a significant methodological advance in multi-fidelity Bayesian optimization, specifically addressing the challenges of high-dimensional black-box functions with constraints. By enabling efficient data usage across arbitrary fidelity levels without nested sampling, this approach directly benefits hyperparameter tuning and engineering design where simulation costs are prohibitive. The demonstrated convergence improvements over existing single- and multi-fidelity methods highlight its potential to accelerate research in machine learning and control systems.
MF-SCBO: Multi-fidelity Scalable Constrained Bayesian Optimization
arXiv:2609.29941v1 Announce Type: new Abstract: Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.
Abstract:Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2609.29941 [cs.LG]
(or arXiv:2609.29941v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.29941 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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