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Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models

US & West · yesterday

arXiv:2510.06439v2 Announce Type: replace-cross Abstract: Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be computationally expensive.

Original headline: Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models

Read the original: arXiv — Computational Engineering, Finance, and Science (cs.CE)

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Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models

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