Kernel-score divergences define a unified family of regression uncertainty measures whose kernel choice controls robustness, tail sensitivity, and OOD responsiveness.
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
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Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Kernel-score divergences define a unified family of regression uncertainty measures whose kernel choice controls robustness, tail sensitivity, and OOD responsiveness.