Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
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 molecul...
| Authors | Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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