arXiv Machine Learning July 14, 2026 By L. A. Zhukov, E. V. Shaburova, D. V. Antonets

Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems

Read the original on arXiv Machine Learning →

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.

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

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