Titre :

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

Auteur(s) :

Schrab, Antonin [Auteur]
Gatsby Computational Neuroscience Unit
Department of Computer science [University College of London] [UCL-CS]
The Inria London Programme [Inria-London]
MOdel for Data Analysis and Learning [MODAL]
University College of London [London] [UCL]
Jitkrittum, Wittawat [Auteur]
Szabó, Zoltán [Auteur]
Sejdinovic, Dino [Auteur]
Gretton, Arthur [Auteur]

Discipline(s) HAL :

Statistiques [stat]/Méthodologie [stat.ME]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Commentaire :

8 pages

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL