Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

• URL: http://arxiv.org/abs/2602.14913v1
• Date: Mon, 16 Feb 2026 16:48:39 GMT
• Title: Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift
• Authors: Farbod Siahkali, Ashwin Verma, Vijay Gupta,
• Abstract summary: Conformal prediction offers marginal coverage guarantees if the data distribution shifts.<n>We analyze the use of pseudo-calibration as a tool to counter this performance loss.<n>We propose a source-tuned pseudo-calibration algorithm that interpolates between hard pseudo-labels and randomized labels.
• Score: 1.5861469511290378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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