CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
Conformal prediction delivers prediction intervals with distribution-free coverage, but its intervals can look overconfident in regions where the model is extrapolating, because standard conformal scores do not explicitly represent epistemic uncertainty. Credal methods, by contrast, make epistemic effects visible by working with sets of plausible predictive distributions, but they are typically model-based and lack calibration guarantees. We introduce CREDO, a simple "credal-then-conformalize" recipe that combines both strengths. CREDO first builds an interpretable credal envelope that widens when local evidence is weak, then applies split conformal calibration on top of this envelope to ...
| Authors | Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende et al. |
|---|---|
| Primary category | stat.ML |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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