Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

Amon Lahr, Anna Scampicchio, Johannes Köhler +1

Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. I…