Sharp Rates of MMD Empirical Estimation with Power Kernels
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914578405785600 |
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| author | Colasanto, Francesco Focardi, Matteo Fornasier, Massimo Mattesini, Francesco |
| author_facet | Colasanto, Francesco Focardi, Matteo Fornasier, Massimo Mattesini, Francesco |
| contents | We establish quantitative rates of convergence for the empirical estimation of probability measures by means of the Maximum Mean Discrepancy (MMD) with power kernel $K_q(x,y) = -|x-y|^q$, $q \in (0,2)$. The resulting discrepancy is the classical energy distance $$\mathcal E_q^2(μ, ω) = -\frac{1}{2}\iint_{\mathbb{R}^d \times \mathbb{R}^d} |x-y|^q \, d(μ- ω)(x)\, d(μ- ω)(y),$$ and we ask how fast the best $N$-point empirical approximation $\inf_{μ_N \in \mathcal{P}^N}\mathcal{E}_q(μ_N,ω)$ decays as $N \to \infty$. Given a probability measure $ω$ on $\mathbb{R}^d$ satisfying an Ahlfors regularity condition of exponent $β$, we prove that the sharp two-sided bound $$\mathcal E_q(μ_N, ω) \asymp N^{-\frac{1}{2}\left(1 + \frac{q}β\right)}$$ holds both for the worst-case empirical measure $μ_N$ (lower bound, holding for every configuration of $N$ points) and for an optimally chosen empirical measure $μ_N$ (upper bound). This complements the qualitative consistency result of Fornasier and Hütter \cite{fornasier2014consistency}, who proved narrow convergence of the minimizers of $\mathcal E_q^2(\cdot, ω)$ over empirical measures without quantitative rates. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_18497 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sharp Rates of MMD Empirical Estimation with Power Kernels Colasanto, Francesco Focardi, Matteo Fornasier, Massimo Mattesini, Francesco Probability Optimization and Control Statistics Theory We establish quantitative rates of convergence for the empirical estimation of probability measures by means of the Maximum Mean Discrepancy (MMD) with power kernel $K_q(x,y) = -|x-y|^q$, $q \in (0,2)$. The resulting discrepancy is the classical energy distance $$\mathcal E_q^2(μ, ω) = -\frac{1}{2}\iint_{\mathbb{R}^d \times \mathbb{R}^d} |x-y|^q \, d(μ- ω)(x)\, d(μ- ω)(y),$$ and we ask how fast the best $N$-point empirical approximation $\inf_{μ_N \in \mathcal{P}^N}\mathcal{E}_q(μ_N,ω)$ decays as $N \to \infty$. Given a probability measure $ω$ on $\mathbb{R}^d$ satisfying an Ahlfors regularity condition of exponent $β$, we prove that the sharp two-sided bound $$\mathcal E_q(μ_N, ω) \asymp N^{-\frac{1}{2}\left(1 + \frac{q}β\right)}$$ holds both for the worst-case empirical measure $μ_N$ (lower bound, holding for every configuration of $N$ points) and for an optimally chosen empirical measure $μ_N$ (upper bound). This complements the qualitative consistency result of Fornasier and Hütter \cite{fornasier2014consistency}, who proved narrow convergence of the minimizers of $\mathcal E_q^2(\cdot, ω)$ over empirical measures without quantitative rates. |
| title | Sharp Rates of MMD Empirical Estimation with Power Kernels |
| topic | Probability Optimization and Control Statistics Theory |
| url | https://arxiv.org/abs/2605.18497 |