# Unbiased estimators for the variance of MMD estimators

@article{Sutherland2019UnbiasedEF, title={Unbiased estimators for the variance of MMD estimators}, author={Danica J. Sutherland}, journal={ArXiv}, year={2019}, volume={abs/1906.02104} }

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to various problems in two-sample testing. Towards this end, Bounliphone et al. (2016) used the theory of U-statistics to derive estimators for the variance of an MMD estimator, and…

## 5 Citations

Maximum Mean Discrepancy is Aware of Adversarial Attacks

- Computer ScienceArXiv
- 2020

It is validated that MMD is aware of adversarial attacks, which lights up a novel road for adversarial attack detection based on two-sample tests.

A Novel Non-parametric Two-Sample Test on Imprecise Observations

- Computer Science2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
- 2020

A fuzzy-based maximum mean discrepancy (F-MMD) is proposed, a powerful two-sample test on imprecise observations that significantly outperforms competitive two- sample test methods when facing imprecising observations.

A Kernel Two-Sample Test for Functional Data

- Computer Science, Mathematics
- 2020

We propose a nonparametric two-sample test procedure based on Maximum Mean Discrepancy (MMD) for testing the hypothesis that two samples of functions have the same underlying distribution, using a…

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

- Computer ScienceICML
- 2021

It is verified that the MMD test is aware of adversarial attacks, which lights up a novel road for adversarial data detection based on two-sample tests.

Learning Deep Kernels for Non-Parametric Two-Sample Tests

- Computer ScienceICML
- 2020

A class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution, which applies both to kernels on deep features and to simpler radial basis kernels or multiple kernel learning.

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