mlsanityData to 7 Oct 2026

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

arXiv 2206.11142 ↗First posted 22 Jun 2022stat.ME cs.LG stat.APcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 22 Jun 2022.

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References · 9

  1. 2017Characteristic and Universal Tensor Product Kernelscited by 41
  2. 2016Large-Scale Kernel Methods for Independence Testingcited by 32
  3. 2014A Wild Bootstrap for Degenerate Kernel Testscited by 28
  4. 2022Efficient Aggregated Kernel Tests using Incomplete U-statisticscited by 27
  5. 2019On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternativescited by 21
  6. 2014A Kernel Independence Test for Random Processescited by 18
  7. 2016An Adaptive Test of Independence with Analytic Kernel Embeddingscited by 13
  8. 2015A simpler condition for consistency of a kernel independence testcited by 8
  9. 2018Multiscale Fisher's Independence Test for Multivariate Dependencecited by 3

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