A simpler condition for consistency of a kernel independence test
Not on the map. Why?
Abstract (arXiv)
A statistical test of independence may be constructed using the Hilbert-Schmidt Independence Criterion (HSIC) as a test statistic. The HSIC is defined as the distance between the embedding of the joint distribution, and the embedding of the product of the marginals, in a Reproducing Kernel Hilbert Space (RKHS). It has previously been shown that when the kernel used in defining the joint embedding is characteristic (that is, the embedding of the joint distribution to the feature space is injective), then the HSIC-based test is consistent. In particular, it is sufficient for the product of kernels on the individual domains to be characteristic on the joint domain. In this note, it is established via a result of Lyons (2013) that HSIC-based independence tests are consistent when kernels on the marginals are characteristic on their respective domains, even when the product of kernels is not characteristic on the joint domain.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jan 2015.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 2; 2025: 0; 2026: 3.
10 papers.
- 2022Efficient Aggregated Kernel Tests using Incomplete U-statisticscited by 27
- 2022Sequential Kernelized Independence Testingcited by 16
- 2024Practical Kernel Tests of Conditional Independencecited by 15
- 2024Robust Kernel Hypothesis Testing under Data Corruptioncited by 7
- 2023Random Smoothing Regularization in Kernel Gradient Descent Learningcited by 6
- 2021Bandits for Learning to Explain from Explanationscited by 1
- 2022Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'cited by 0
- 2026Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independencecited by 0
- 2026Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneitycited by 0
- 2026Aggregation of Statistical Evidence under Exchangeabilitycited by 0
- 2026Aggregation of Statistical Evidence under Exchangeability17 Jul 2026
- 2026Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity26 May 2026
- 2026Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence8 Apr 2026
- 2024Robust Kernel Hypothesis Testing under Data Corruption30 May 2024
- 2024Practical Kernel Tests of Conditional Independence20 Feb 2024
- 2023Random Smoothing Regularization in Kernel Gradient Descent Learning5 May 2023
- 2022Sequential Kernelized Independence Testing14 Dec 2022
- 2022Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'22 Jun 2022
- 2022Efficient Aggregated Kernel Tests using Incomplete U-statistics18 Jun 2022
- 2021Bandits for Learning to Explain from Explanations7 Feb 2021