[Submitted on 9 Sep 2026]
Title:Conformal Calibration Transfer
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Abstract:Conformal prediction converts point predictions into set-valued predictions with coverage guarantees under exchangeability between calibration and deployment data. We study conformal calibration transfer, where this requirement fails because labeled calibration is available only in a source space, while prediction sets are needed in a target space linked to the source through unlabeled paired observations (e.g., paired modalities or sensor changes). We propose Transported Conformal Calibration (TCC): we transport labeled source calibration into the target space using the paired data, and then correct residual post-transport mismatch using only unlabeled target inputs. We instantiate this correction with two complementary methods: TCC-KS, which uses a label-free uncertainty surrogate to detect mismatch and adjust calibration conservatively, and weighted-TCC, which reweights transported calibration toward the target domain for improved efficiency when weights are stable. We provide finite-sample target-domain coverage guarantees that adapt to an observable measure of mismatch. Across CIFAR-100-C, Tiny-ImageNet-C, and SEN12MS, we show reliable target-domain coverage transfer without labeled target calibration data, with label-free diagnostics that predict when correction is needed.
Comments: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2609.10737 [cs.LG]
(or arXiv:2609.10737v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.10737
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Achref Doula [view email] [v1] Wed, 9 Sep 2026 18:32:12 UTC (126 KB)
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