[2510.04318] Adaptive Coverage Policies in Conformal Prediction

Statistics > Machine Learning arXiv:2510.04318 (stat) [Submitted on 5 Oct 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:Adaptive Coverage Policies in Conformal Prediction Authors:Etienne Gauthier, Francis Bach, Michael I. Jordan View a PDF of the paper titled Adaptive Coverage Policies in Conformal Prediction, by Etienne Gauthier and 2 other authors View PDF HTML (experimental) Abstract:Traditional conformal prediction methods construct prediction sets such that the true label falls within the set with a user-specified coverage level. However, poorly chosen coverage levels can result in uninformative predictions, either producing overly conservative sets when the coverage level is too high, or empty sets when it is too low. Moreover, the fixed coverage level cannot adapt to the specific characteristics of each individual example, limiting the flexibility and efficiency of these methods. In this work, we leverage recent advances in e-values and post-hoc conformal inference, which allow the use of data-dependent coverage levels while maintaining valid statistical guarantees. We propose to optimize an adaptive coverage policy by training a neural network using a leave-one-out procedure on the calibration set, allowing the coverage level and the resulting prediction set size to vary with the difficulty of each individual example. We support our approach with theoretical coverage guarantees and demonstrate its practical benefits through a series of experiments. C...