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Coverage Guarantees for Pseudo-Calibrated Conformal Predi...
[Submitted on 16 Feb 2026 (v1), last revised 16 Jul 2026 (this v · 2026-06-11 · via cs.LG updates on arXiv.org

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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.

Submission history

From: Farbod Siahkali [view email]
[v1] Mon, 16 Feb 2026 16:48:39 UTC (286 KB)
[v2] Wed, 10 Jun 2026 15:16:30 UTC (3,781 KB)
[v3] Thu, 16 Jul 2026 18:57:11 UTC (3,766 KB)