[2603.24704] Conformal Selective Prediction with General Risk Control

Statistics > Methodology arXiv:2603.24704 (stat) [Submitted on 25 Mar 2026] Title:Conformal Selective Prediction with General Risk Control Authors:Tian Bai, Ying Jin View a PDF of the paper titled Conformal Selective Prediction with General Risk Control, by Tian Bai and 1 other authors View PDF HTML (experimental) Abstract:In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted. We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We eval...