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Conformal Risk Control for monotone bounded prediction losses

Contribution grade

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

Reference score155,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Adds a finite-sample worst-case correction to held-out empirical loss and selects conservativeness to control expected loss on a future exchangeable example.

Scientific scope

Adds a finite-sample worst-case correction to held-out empirical loss and selects conservativeness to control expected loss on a future exchangeable example. Expectation is over calibration and test randomness, unlike high-probability calibration-conditional RCPS. Requires exchangeable, non-increasing, right-continuous losses with stated upper bound and feasible endpoint; arbitrary non-monotone losses are explicitly not covered. Near-tight lower bound has additional assumptions.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Expectation is over calibration and test randomness, unlike high-probability calibration-conditional RCPS. Requires exchangeable, non-increasing, right-continuous losses with stated upper bound and feasible endpoint; arbitrary non-monotone losses are explicitly not covered. Near-tight lower bound has additional assumptions.

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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