Adaptive Set-Mass Calibration with Conformal Prediction

• URL: http://arxiv.org/abs/2505.15437v2
• Date: Thu, 16 Oct 2025 13:39:56 GMT
• Title: Adaptive Set-Mass Calibration with Conformal Prediction
• Authors: Daniil Kazantsev, Mohsen Guizani, Eric Moulines, Maxim Panov, Nikita Kotelevskii,
• Abstract summary: We develop a new calibration procedure that starts with conformal prediction to obtain a set of labels that gives the desired coverage.<n>We then instantiate two simple post-hoc calibrators: a mass normalization and a temperature scaling-based rule, tuned to the conformal constraint.
• Score: 60.47079469141295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reliable probabilities are critical in high-risk applications, yet common calibration criteria (confidence, class-wise) are only necessary for full distributional calibration, and post-hoc methods often lack distribution-free guarantees. We propose a set-based notion of calibration, cumulative mass calibration, and a corresponding empirical error measure: the Cumulative Mass Calibration Error (CMCE). We develop a new calibration procedure that starts with conformal prediction to obtain a set of labels that gives the desired coverage. We then instantiate two simple post-hoc calibrators: a mass normalization and a temperature scaling-based rule, tuned to the conformal constraint. On multi-class image benchmarks, especially with a large number of classes, our methods consistently improve CMCE and standard metrics (ECE, cw-ECE, MCE) over baselines, delivering a practical, scalable framework with theoretical guarantees.

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