arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.
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.
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.
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.
arXiv:2609.36472v1 Announce Type: new Abstract: Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically...
The paper introduces Bayesian Optimization (BO) techniques that incorporate rich auxiliary information—such as training curves, expert notes, images, and prior knowledge—using large language models (LLMs). Three new methods are proposed to integrate this auxiliary data into BO, and they are evaluated on hyperparameter optimization benchmarks and a real-world nuclear fusion task. The results show that these LLM-enhanced BO methods consistently outperform standard BO and existing LLM-based optimization approaches.
The paper presents a method that uses Constrained Bayesian Optimization (CBO) to minimize the energy consumption of machine learning models while ensuring their generalization performance stays above a specified threshold. By treating energy usage as the primary objective and performance as a constraint, the authors demonstrate that CBO can reduce training energy costs on both regression and classification tasks without sacrificing predictive accuracy.
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
arXiv:2606. 08611v1 Announce Type: cross Abstract: We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance.
arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.
arXiv:2609.08620v1 Announce Type: cross Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learni...
arXiv:2607. 00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small.