Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction
Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable. Randomly localized conformal prediction (RLCP) mitigates this gap by calibrating near the test point while preserving marginal coverage. Existing theory, however, lacks finite-sample guarantees for the realized localized set that jointly control conditional validity and oracle efficiency. We provide such guarantees. For any fixed score, under Hölder regularity of the conditional score CDF and standard density and kernel as...
| Authors | Anton Conrad, Rustam Isaev, Denis Belomestny et al. |
|---|---|
| Primary category | stat.ML |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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