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Selection with Guaranteed Risk for pretrained deep 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

Searches a confidence threshold using labeled calibration data and binomial risk upper bounds to trade accepted-example coverage against selective error.

Scientific scope

Searches a confidence threshold using labeled calibration data and binomial risk upper bounds to trade accepted-example coverage against selective error. General selective-prediction idea and binomial bounds are inherited. Source risk guarantee depends on its i.i.d./calibration and fixed-model/confidence/search setup, not arbitrary reuse or shift; no independent proof certification.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. General selective-prediction idea and binomial bounds are inherited. Source risk guarantee depends on its i.i.d./calibration and fixed-model/confidence/search setup, not arbitrary reuse or shift; no independent proof certification.

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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