AWhat changed
Forms prediction-interval endpoints from leave-one-out predictions at the new input paired with their held-out residuals, obtaining a distribution-free coverage guarantee for symmetric learning algorithms.
Scientific scope
Forms prediction-interval endpoints from leave-one-out predictions at the new input paired with their held-out residuals, obtaining a distribution-free coverage guarantee for symmetric learning algorithms. Under exchangeable data and permutation-invariant fitting, the worst-case coverage guarantee is 1−2α for a target of 1−α. Near-nominal theoretical coverage requires additional stability conditions; empirical near-nominal behavior is not a universal guarantee.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Under exchangeable data and permutation-invariant fitting, the worst-case coverage guarantee is 1−2α for a target of 1−α. Near-nominal theoretical coverage requires additional stability conditions; empirical near-nominal behavior is not a universal guarantee.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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