ConformaDecompose: Explaining Uncertainty via Calibration Localization
📰 ArXiv cs.AI
Learn to explain uncertainty in Conformal Prediction using ConformaDecompose, a method that localizes calibration to provide insight into sources of uncertainty
Action Steps
- Apply ConformaDecompose to a Conformal Prediction model to decompose uncertainty into aleatoric and epistemic components
- Use calibration localization to identify sources of uncertainty at the instance level
- Evaluate the effectiveness of ConformaDecompose in explaining uncertainty using metrics such as coverage and interval width
- Compare the performance of ConformaDecompose with other uncertainty quantification methods
- Integrate ConformaDecompose into a larger framework for model explainability and interpretability
Who Needs to Know This
Data scientists and machine learning engineers working with uncertainty quantification and explainability can benefit from this method to improve model interpretability and trustworthiness
Key Insight
💡 ConformaDecompose provides a way to decompose uncertainty into aleatoric and epistemic components, allowing for more informative and actionable explanations of model uncertainty
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🚀 Introducing ConformaDecompose: explaining uncertainty in Conformal Prediction via calibration localization #UncertaintyQuantification #Explainability
Key Takeaways
Learn to explain uncertainty in Conformal Prediction using ConformaDecompose, a method that localizes calibration to provide insight into sources of uncertainty
Full Article
Title: ConformaDecompose: Explaining Uncertainty via Calibration Localization
Abstract:
arXiv:2604.27149v1 Announce Type: cross Abstract: Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration threshold obscures the sources of uncertainty at the instance level. In particular, it conflates irreducible noise with uncertainty induced by heterogeneous training data (aleatoric), model limitations, or calibration mismatch (epistemic), offering little insight into why an interval is wide or whether it
Abstract:
arXiv:2604.27149v1 Announce Type: cross Abstract: Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration threshold obscures the sources of uncertainty at the instance level. In particular, it conflates irreducible noise with uncertainty induced by heterogeneous training data (aleatoric), model limitations, or calibration mismatch (epistemic), offering little insight into why an interval is wide or whether it
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