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stat.ML2024
Towards safe Bayesian optimization with Wiener kernel regression
Oleksii Molodchyk, Johannes Teutsch, Timm Faulwasser
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, t…
stat.ML2023
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty
T. Faulwasser, O. Molodchyk
Gaussian Processes (GPs) are a versatile method that enables different approaches towards learning for dynamics and control. Gaussianity assumptions appear in two dimensions in GPs…