E-Values Expand the Scope of Conformal Prediction

• URL: http://arxiv.org/abs/2503.13050v2
• Date: Tue, 18 Mar 2025 09:51:46 GMT
• Title: E-Values Expand the Scope of Conformal Prediction
• Authors: Etienne Gauthier, Francis Bach, Michael I. Jordan,
• Abstract summary: Conformal prediction is a powerful framework for distribution-free uncertainty quantification.<n>In this paper, we explore an alternative approach based on e-values, known as conformal e-prediction.<n>E-values offer key advantages that cannot be achieved with p-values, enabling new theoretical and practical capabilities.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction is a powerful framework for distribution-free uncertainty quantification. The standard approach to conformal prediction relies on comparing the ranks of prediction scores: under exchangeability, the rank of a future test point cannot be too extreme relative to a calibration set. This rank-based method can be reformulated in terms of p-values. In this paper, we explore an alternative approach based on e-values, known as conformal e-prediction. E-values offer key advantages that cannot be achieved with p-values, enabling new theoretical and practical capabilities. In particular, we present three applications that leverage the unique strengths of e-values: batch anytime-valid conformal prediction, fixed-size conformal sets with data-dependent coverage, and conformal prediction under ambiguous ground truth. Overall, these examples demonstrate that e-value-based constructions provide a flexible expansion of the toolbox of conformal prediction.

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