Author

G. Sutter

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#artificial intelligence Preprint Oct 2026

Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization

Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency has two consequences during optimization: (i) the objective evaluation dominates execution time, and (ii) sample-efficient algorithms are crucial to accelerate developm...

G. Sutter, A. Comas-Leon, David Holzmüller et al. · 0 citations

Preprint Aug 2026

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

This work demonstrates the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions, and mitigate this problem by incorporating historical high-fidelity data accompanied by task descriptors---which can be explicitly given or extracted from unstructured metadata.

G. Sutter, Hao Wang, Luis A. Ricardez-Sandoval et al. · 0 citations