Conformal prediction without knowledge of labeled calibration data

Jonas Flechsig; Maximilian Pilz

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arXiv · 2509.10321

Conformal prediction without knowledge of labeled calibration data

Jonas Flechsig·Maximilian Pilz

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Abstract

We extend the method of conformal prediction beyond the case relying on labeled calibration data. Replacing the calibration scores by suitable estimates, we identify conformity sets $C$ for classification and regression models that rely on unlabeled calibration data. Given a classification model with accuracy $1-\beta$, we prove that the conformity sets guarantee a coverage of $P(Y \in C) \geq 1-\alpha-\beta$ for an arbitrary parameter $\alpha \in (0,1)$. The same coverage guarantee also holds for regression models, if we replace the accuracy by a similar exactness measure. Finally, we describe how to use the theoretical results in practice.

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BibTeX RIS

Jonas Flechsig, Maximilian Pilz. 2025-09-12. Conformal prediction without knowledge of labeled calibration data. https://arxiv.org/abs/2509.10321

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