Author

Anna Scampicchio

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Preprint Aug 2026

Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control

This work formalizes a deterministic trigonometric feature Gaussian process (DTF-GP), a finite-dimensional kernel approximation based on discretized trigonometric features that reduces GP regression to Bayesian linear regression in feature space, and derives a high-probability uniform uncertainty bound for the proposed DTF-GP.

Julius Jagdt, Johanna Menn, Sebastian Trimpe et al. · 0 citations