Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
PositiveArtificial Intelligence
A new framework for uncertainty quantification in regression tasks has been introduced, addressing a significant gap in the literature that has primarily focused on classification. This framework emphasizes kernel scores and offers a unified approach to measuring total, aleatoric, and epistemic uncertainty. This is particularly important for safety-critical domains where understanding uncertainty can lead to better decision-making and improved outcomes.
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