Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means

The Median of Means (MoM) is a mean estimator that has gained popularity in the context of heavy-tailed data. In this work, we analyze its performance in the task of simultaneously estimating the mean of each function in a class $\mathcal{F}$ when the data distribution possesses only the first $p$ moments for $p \in (1,2]$. We prove a new sample complexity bound using a novel symmetrization technique that may be of independent interest. Additionally, we present applications of our result to $k$-means clustering with unbounded inputs and linear regression with general losses, improving upon existing works.

@article{høgsgaard2025_2506.14673,
  title={ Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means },
  author={ Mikael Møller Høgsgaard and Andrea Paudice },
  journal={arXiv preprint arXiv:2506.14673},
  year={ 2025 }
}