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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.14711 |
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| _version_ | 1866912652774604800 |
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| author | Glaser, Pierre Widmann, David Lindsten, Fredrik Gretton, Arthur |
| author_facet | Glaser, Pierre Widmann, David Lindsten, Fredrik Gretton, Arthur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14711 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Scalable Score-Based Kernel Calibration Tests Glaser, Pierre Widmann, David Lindsten, Fredrik Gretton, Arthur Machine Learning 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. |
| title | Fast and Scalable Score-Based Kernel Calibration Tests |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.14711 |