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Discussion of `Multiscale Fisher’s Independence Test for Multivariate Dependence’



Schrab, Antonin;

Jitkrittum, Wittawat;

Szabó, Zoltán;

Sejdinovic, Dino;

Gretton, Arthur;

(2022)

Discussion of `Multiscale Fisher’s Independence Test for Multivariate Dependence’.

arXiv


10.48550/arXiv.2206.11142.

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    Abstract

    We discuss how MultiFIT, the Multiscale Fisher’s Independence Test for
    Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing
    linear-time kernel tests based on the Hilbert-Schmidt independence criterion
    (HSIC). We highlight the fact that the levels of the kernel tests at any finite
    sample size can be controlled exactly, as it is the case with the level of
    MultiFIT. In our experiments, we observe some of the performance limitations of
    MultiFIT in terms of test power.

    Type: Article

    Title: Discussion of `Multiscale Fisher’s Independence Test for Multivariate Dependence’
    Open access status: An open access version is available from UCL Discovery
    DOI: 10.48550/arXiv.2206.11142
    Publisher version: https://doi.org/10.48550/arXiv.2206.11142
    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.
    Keywords: Methodology (stat.ME); Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO); Machine Learning (stat.ML)
    UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
    UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
    UCL > Provost and Vice Provost Offices > UCL BEAMS
    UCL
    URI: https://discovery.ucl.ac.uk/id/eprint/10151077

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