A conformalized kernel-depth algorithm constructs prediction regions for regression in separable Hilbert spaces with marginal non-asymptotic guarantees and asymptotic conditional consistency.
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Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
A conformalized kernel-depth algorithm constructs prediction regions for regression in separable Hilbert spaces with marginal non-asymptotic guarantees and asymptotic conditional consistency.