Generalized Conformal Predictive Systems Under Distributional Shifts

Abstract:Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability. We extend generalized CPS to non-exchangeable settings by encoding distributional shifts through observation-specific permutation weights. This yields shift-aware predictive systems that remain valid whenever the test point is, conditionally on the unordered sample, a weighted draw from the observed atoms. Since such weights are typically estimated, we introduce weight-uncertainty boxes and construct robust CPS envelopes with finite-sample or asymptotic confidence guarantees. We derive efficient computation for conformity-measure CPS, conformal binning, and conformal isotonic distributional regression. Experiments under covariate shift and feedback-driven biomolecular design show calibrated predictive bands that widen under stronger shifts and tighten as sample size increases.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2606.11044 [stat.ML]

(or arXiv:2606.11044v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2606.11044 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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