aiqbench analysis
This research addresses the critical challenge of maintaining statistical validity in conformal prediction when labeled calibration data exists only in a source domain, not the target deployment environment. The proposed Transported Conformal Calibration framework successfully leverages unlabeled paired observations to transfer coverage guarantees across domains like sensor shifts or modality changes without requiring new labeled data. This advancement is significant for deploying robust AI systems in dynamic real-world settings where acquiring fresh calibration labels is often impractical.
Conformal Calibration Transfer
arXiv:2609.10737v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2609.10737 [cs.LG]
(or arXiv:2609.10737v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.10737 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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