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A Kernel Stein Test of Goodness of Fit for Sequential Models

Baume, Jerome; Kanagawa, Heishiro; Gretton, Arthur; (2023) A Kernel Stein Test of Goodness of Fit for Sequential Models. In: Proceedings of the International Conference on Machine Learning. ICML Proceedings (In press). Green open access

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Abstract

We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein operator: current operators used in testing apply to fixed-dimensional spaces. As our main contribution, we extend the KSD to the variabledimension setting by identifying appropriate Stein operators, and propose a novel KSD goodness-offit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class of models. Our test is shown to perform well in practice on discrete sequential data benchmarks.

Type: Proceedings paper
Title: A Kernel Stein Test of Goodness of Fit for Sequential Models
Event: International Conference on Machine Learning (ICML)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://icml.cc/virtual/2023/papers.html?filter=ti...
Language: English
Additional information: This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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/10173060
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