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arXiv Machine Learning By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

Read the original on arXiv Machine Learning →

arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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