UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Fast and Scalable Score-Based Kernel Calibration Tests

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. Green open access

[thumbnail of glaser23a.pdf]
Preview
Text
glaser23a.pdf - Published Version

Download (365kB) | Preview

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
Downloads since deposit
5Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item