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arXiv Machine Learning By Lucas Palazzolo, Micka\"el Binois, La\"etitia Giraldi

MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization

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MF-SCBO is a new multi‑fidelity extension of Scalable Constrained Bayesian Optimization designed for high‑dimensional black‑box functions with black‑box constraints. It handles an arbitrary number of fidelity levels and non‑nested sampling, addressing gaps in existing methods. Experiments on standard benchmarks and challenging problems show that MF‑SCBO generally converges faster than both single‑fidelity SCBO and other multi‑fidelity approaches in high‑dimensional constrained settings.

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