# Minimum Kernel Discrepancy Estimators

arXiv: https://arxiv.org/abs/2210.16357 · HTML: https://mlsanity.com/paper/2210.16357/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [Score-based deterministic density sampling](https://mlsanity.com/paper/2504.18130/) (2025)
- [A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks](https://mlsanity.com/paper/2303.02637/) (2023)
- [General reproducing properties in RKHS with application to derivative and integral operators](https://mlsanity.com/paper/2503.15922/) (2025)
- [A Gradient Flow Perspective on Minimum MMD Estimation](https://mlsanity.com/paper/2607.03871/) (2026)

## Abstract (arXiv)

For two decades, reproducing kernels and their associated discrepancies have facilitated elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now receiving interest in statistics and related fields, as criteria that can be used to select an appropriate statistical model for a given dataset. The focus of this article is on minimum kernel discrepancy estimators, whose use in statistical applications is reviewed, and a general theoretical framework for establishing their asymptotic properties is presented.