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RAPS: regularized adaptive prediction sets for image classifiers

Contribution grade

AI uncertainty, reliability and selective prediction · Human-designed research method or resource

Reference score125,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Penalizes cumulative ranked class probabilities beyond a chosen rank and calibrates the resulting nested randomized sets on held-out data to control marginal label coverage.

Scientific scope

Penalizes cumulative ranked class probabilities beyond a chosen rank and calibrates the resulting nested randomized sets on held-out data to control marginal label coverage. Inherits conformal validity and APS cumulative scores; new rank-tail regularization is distinct. Finite-sample theorem assumes i.i.d. calibration/test triples and nested sets, with positivity for near-tight upper coverage. Marginal coverage is not per-input conditional coverage or guaranteed minimal set size.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits conformal validity and APS cumulative scores; new rank-tail regularization is distinct. Finite-sample theorem assumes i.i.d. calibration/test triples and nested sets, with positivity for near-tight upper coverage. Marginal coverage is not per-input conditional coverage or guaranteed minimal set size.

Contributors

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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