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arXiv · 2510.14711

Fast and Scalable Score-Based Kernel Calibration Tests

Pierre Glaser·David Widmann·Fredrik Lindsten·Arthur Gretton
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Abstract

We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with well-defined scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We achieve these improvements by using a new family of kernels for score-based probabilities that can be estimated without probability density samples, and by using a conditional goodness-of-fit criterion for the KCCSD test's U-statistic. We demonstrate the properties of our test on various synthetic settings.

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Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton. 2025-10-16. Fast and Scalable Score-Based Kernel Calibration Tests. https://arxiv.org/abs/2510.14711

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