Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

Abstract:Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts. We analyze the use of pseudo-calibration as a tool to counter this performance loss under a bounded label-conditional covariate shift model. Using tools from domain adaptation, we derive a lower bound on target coverage in terms of the source-domain loss of the classifier and a Wasserstein measure of the shift. Using this result, we provide a method to design pseudo-calibrated sets that inflate the conformal threshold by a slack parameter to keep target coverage above a prescribed level. Finally, we propose a source-tuned pseudo-calibration algorithm that interpolates between hard pseudo-labels and randomized labels as a function of classifier uncertainty. Numerical experiments show that our bounds qualitatively track pseudo-calibration behavior and that the source-tuned scheme mitigates coverage degradation under distribution shift while maintaining nontrivial prediction set sizes.

Under review. 6 pages, 2 figures, 1 table

Machine Learning (cs.LG); Image and Video Processing (eess.IV)

arXiv:2602.14913 [cs.LG]

(or arXiv:2602.14913v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.14913 Focus to learn more arXiv-issued DOI via DataCite

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