Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction

• URL: http://arxiv.org/abs/2502.05676v3
• Date: Tue, 15 Jul 2025 22:50:21 GMT
• Title: Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
• Authors: Lars van der Laan, Ahmed Alaa,
• Abstract summary: We introduce a unified framework for Venn-Abers calibration that extends Vovk's approach beyond binary classification.<n>Our method transforms any perfectly in-sample calibrated predictor into a set-valued predictor that, in finite samples, outputs at least one marginally point prediction.<n>For quantile loss, our framework recovers group-conditional and multicalibrated conformal prediction as special cases and yields novel prediction intervals with quantile-conditional coverage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring model calibration is critical for reliable prediction, yet popular distribution-free methods such as histogram binning and isotonic regression offer only asymptotic guarantees. We introduce a unified framework for Venn and Venn-Abers calibration that extends Vovk's approach beyond binary classification to a broad class of prediction problems defined by generic loss functions. Our method transforms any perfectly in-sample calibrated predictor into a set-valued predictor that, in finite samples, outputs at least one marginally calibrated point prediction. These set predictions shrink asymptotically and converge to a single conditionally calibrated prediction, capturing epistemic uncertainty. We further propose Venn multicalibration, a new approach for achieving finite-sample calibration across subpopulations. For quantile loss, our framework recovers group-conditional and multicalibrated conformal prediction as special cases and yields novel prediction intervals with quantile-conditional coverage.

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