Out-Of-The-Loop Multi-Fidelity Bayesian Optimization

• URL: http://arxiv.org/abs/2608.04113v1
• Date: Tue, 04 Aug 2026 18:07:26 GMT
• Title: Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
• Abstract summary: Black-box optimization is a ubiquitous problem in science and engineering.<n>MF-BO is a principled approach to this problem, leveraging correlations across different fidelities when querying the objective.<n>We demonstrate the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions.
• Score: 19.257011368308326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available. Multi-fidelity Bayesian optimization (MF-BO) is a principled approach to this problem, leveraging correlations across different fidelities when querying the objective. However, for many important MF-BO tasks, the true highest-fidelity function is prohibitively expensive to be part of the optimization loop. Nevertheless, practitioners often have gold standard data (observations of the highest-fidelity function) obtained from previous experiments that might provide information for the current task. For instance, in molecular optimization, chemists often pick the top-$k$ candidate molecules using various computer simulations, and later reveal their true objective function values. In this work, we demonstrate the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions. Next, we mitigate this problem by incorporating historical high-fidelity data accompanied by task descriptors---which can be explicitly given or extracted from unstructured metadata. We demonstrate the effectiveness of our methods on synthetic functions, as well as real-world problems in chemistry and hyperparameter optimization.

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