Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
The framework proposes a unified method for quantifying uncertainty in regression tasks by basing measures on strictly proper kernel scores. This approach designs new uncertainty metrics whose behavior is controlled by the choice of kernel, offering a principled way to handle structured data and improve out-of-distribution detection.