Conformal Selective Prediction with General Risk Control
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 (general...
| Authors | Tian Bai, Ying Jin |
|---|---|
| Primary category | stat.ME |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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