arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.
By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
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.
By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo
arXiv:2609.26021v1 Announce Type: new
Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...
By Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho
arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.
By Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros, Grigorios M. Chatziathanasiou, Efi-Maria Papia, George Giannakopoulos
arXiv:2607. 00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small.
By Hauke Maathuis, Roeland De Breuker, Saullo Castro, Maike Osborne
arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.
By Marc Becker, Lennart Schneider, Martin Binder, Lars Kotthoff, Bernd Bischl