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,
- 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.