Glaser, P;
Widmann, D;
Lindsten, F;
Gretton, A;
(2023)
Fast and Scalable Score-Based Kernel Calibration Tests.
In:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 691-700).
PMLR: Pittsburgh, PA, USA.
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Abstract
We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a nonparametric, 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.
| Type: | Proceedings paper |
|---|---|
| Title: | Fast and Scalable Score-Based Kernel Calibration Tests |
| Event: | 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v216/glaser23a.html |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10177641 |
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