Conformal Prediction Uncertainty Explained via Calibration Localization
The newly developed framework, ConformaDecompose, evaluates the reducibility of epistemic conformal uncertainty induced by calibration through a methodical calibration localization for regression tasks. This approach serves a diagnostic purpose rather than a causal one, clarifying how conformal intervals can contract and stabilize when calibration support is focused around a specific test instance, without estimating the actual aleatoric or epistemic uncertainty. It overcomes a key limitation of traditional Conformal Prediction, which uses a single global calibration threshold that masks instance-specific uncertainty sources, merging irreducible noise with uncertainty stemming from diverse training data, model constraints, or calibration discrepancies. The framework was validated on various benchmarks and real-world datasets, revealing insights into the width of intervals and their potential for reduction. The findings are detailed in a paper available on arXiv (2604.27149).
Key facts
- ConformaDecompose analyzes calibration-induced epistemic conformal uncertainty via progressive calibration localization.
- The framework is diagnostic, not causal, explaining how conformal intervals contract and stabilize.
- It does not estimate true aleatoric or epistemic uncertainty.
- Standard Conformal Prediction uses a single global calibration threshold.
- The approach addresses conflation of irreducible noise with uncertainty from heterogeneous data, model limitations, or calibration mismatch.
- Tested across benchmarks and real-world data.
- Paper available on arXiv with ID 2604.27149.
- The method is for regression tasks.
Entities
Institutions
- arXiv