ConformaDecompose: Explaining Uncertainty via Calibration Localization
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913073415061504 |
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| author | Yapicioglu, Fatima Rabia Aksoy, Meltem Rigenti, Alberto Löfström-Cavallin, Tuwe Löfström-Cavallin, Helena Yoncaci, Seyda Longo, Luca |
| author_facet | Yapicioglu, Fatima Rabia Aksoy, Meltem Rigenti, Alberto Löfström-Cavallin, Tuwe Löfström-Cavallin, Helena Yoncaci, Seyda Longo, Luca |
| contents | 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 could be reduced. We introduce an uncertainty-aware explainability framework that analyses the reducibility of calibration-induced epistemic conformal uncertainty via progressive calibration localisation for regression tasks. The approach is diagnostic rather than causal: it does not estimate true aleatoric or epistemic uncertainty, but explains how conformal intervals contract and stabilise as calibration support is localised around a test instance. Across benchmarks and real-world data, absolute reducible uncertainty aligns with epistemic proxies, while its relative contribution varies by task, revealing regimes hidden by interval width. This instance-level view complements conformal uncertainty, enhancing interpretability without altering the predictor or coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27149 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ConformaDecompose: Explaining Uncertainty via Calibration Localization Yapicioglu, Fatima Rabia Aksoy, Meltem Rigenti, Alberto Löfström-Cavallin, Tuwe Löfström-Cavallin, Helena Yoncaci, Seyda Longo, Luca Machine Learning Artificial Intelligence 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 could be reduced. We introduce an uncertainty-aware explainability framework that analyses the reducibility of calibration-induced epistemic conformal uncertainty via progressive calibration localisation for regression tasks. The approach is diagnostic rather than causal: it does not estimate true aleatoric or epistemic uncertainty, but explains how conformal intervals contract and stabilise as calibration support is localised around a test instance. Across benchmarks and real-world data, absolute reducible uncertainty aligns with epistemic proxies, while its relative contribution varies by task, revealing regimes hidden by interval width. This instance-level view complements conformal uncertainty, enhancing interpretability without altering the predictor or coverage. |
| title | ConformaDecompose: Explaining Uncertainty via Calibration Localization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.27149 |