Generalized Conformal Predictive Systems Under Distributional Shifts
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 regressio...
| Authors | Jef Jonkers, Johanna Ziegel |
|---|---|
| Primary category | stat.ML |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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