Conformal Prediction as Bayesian Quadrature

• URL: http://arxiv.org/abs/2502.13228v1
• Date: Tue, 18 Feb 2025 19:06:21 GMT
• Title: Conformal Prediction as Bayesian Quadrature
• Authors: Jake C. Snell, Thomas L. Griffiths,
• Abstract summary: Distribution-free uncertainty quantification techniques such as conformal prediction provide guarantees about the loss black-box models will incur. We revisit the central aspects of conformal prediction from a Bayesian perspective and illuminate the shortcomings of frequentist guarantees. We propose a practical alternative based on Bayesian quadrature that provides interpretable guarantees and offers a richer representation of the likely range of losses to be observed at test time.
• Score: 4.767884267554628
• License:
• Abstract: As machine learning-based prediction systems are increasingly used in high-stakes situations, it is important to understand how such predictive models will perform upon deployment. Distribution-free uncertainty quantification techniques such as conformal prediction provide guarantees about the loss black-box models will incur even when the details of the models are hidden. However, such methods are based on frequentist probability, which unduly limits their applicability. We revisit the central aspects of conformal prediction from a Bayesian perspective and thereby illuminate the shortcomings of frequentist guarantees. We propose a practical alternative based on Bayesian quadrature that provides interpretable guarantees and offers a richer representation of the likely range of losses to be observed at test time.

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