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Multi-Fidelity Cost-Aware Bayesian Optimization. (arXiv:2211.02732v1 [stat.ML])
Nov. 8, 2022, 2:13 a.m. | Zahra Zanjani Foumani, Mehdi Shishehbor, Amin Yousefpour, Ramin Bostanabad
stat.ML updates on arXiv.org arxiv.org
Bayesian optimization (BO) is increasingly employed in critical applications
such as materials design and drug discovery. An increasingly popular strategy
in BO is to forgo the sole reliance on high-fidelity data and instead use an
ensemble of information sources which provide inexpensive low-fidelity data.
The overall premise of this strategy is to reduce the overall sampling costs by
querying inexpensive low-fidelity sources whose data are correlated with
high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO
framework that dramatically outperforms …
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