Subjective Risk Decomposition: A New View for Uncertainty Quantification
Merged summary
TL;DR - A preprint proposing that uncertainty measures aren't axiomatic primitives but consequences of modelling choices, deriving epistemic and aleatoric uncertainty by decomposing a "subjective risk" built from a strictly proper loss. It matters because it unifies many scattered UQ measures under one theoretical foundation and links them to learning theory.
- Reframes uncertainty quantification (UQ): given a modelling scenario and a strictly proper loss, the epistemic/aleatoric terms are induced by decomposing the subjective risk rather than postulated.
- Reverse cross-entropy is a headline example, where the decomposition recovers the classic information-theoretic uncertainty terms.
- Claims the same approach reproduces numerous previously proposed UQ measures, giving them a common grounding.
- Extends to learning theory via subjective-risk analogues of excess risk, approximation error, and estimation error, positioned as a first step toward a learning-theoretic UQ framework.
Note: summary is based solely on the abstract; no empirical results or benchmarks are provided in the content.
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Subjective Risk Decomposition: A New View for Uncertainty Quantification
TL;DR - A preprint proposing that uncertainty measures aren't axiomatic primitives but consequences of modelling choices, deriving epistemic and aleatoric uncertainty by decomposing a "subjective risk" built from a strictly proper loss. It matters because it unifies many scattered UQ measures under one theoretical foundation and links them to learning theory.
- Reframes uncertainty quantification (UQ): given a modelling scenario and a strictly proper loss, the epistemic/aleatoric terms are induced by decomposing the subjective risk rather than postulated.
- Reverse cross-entropy is a headline example, where the decomposition recovers the classic information-theoretic uncertainty terms.
- Claims the same approach reproduces numerous previously proposed UQ measures, giving them a common grounding.
- Extends to learning theory via subjective-risk analogues of excess risk, approximation error, and estimation error, positioned as a first step toward a learning-theoretic UQ framework.
Note: summary is based solely on the abstract; no empirical results or benchmarks are provided in the content.