Subjective Risk Decomposition: A New View for Uncertainty Quantification
We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling decisions. We show how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss. Reverse cross-entropy provides a prominent example, where decomposition recovers the classic information-theoretic uncertainty terms. The same approach recovers numerous measures previously proposed across the UQ literature, providing them a common theo
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- PossiblePossibly related (embedding) · 48%Uncertainty Quantification for LLM Function-Calling - Apple Machine Learning Research →
- LinkedLinked via arxiv author · 85%Raghad Alamri →
“Subjective Risk Decomposition: A New View for Uncertainty Quantification”
- LinkedLinked via arxiv author · 85%Michele Caprio →
“Subjective Risk Decomposition: A New View for Uncertainty Quantification”
- LinkedLinked via arxiv author · 85%Gavin Brown →
“Subjective Risk Decomposition: A New View for Uncertainty Quantification”