We study the problem of heavy-tailed mean estimation in settings where the
variance of the data-generating distribution does not exist. Concretely, given
a sample $\mathbf{X} = \{X_i\}_{i = 1}^n$ from a distribution $\mathcal{D}$
over $\mathbb{R}^d$ with mean $\mu$ which satisfies the