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# Nearly Optimal Catoni’s M-estimator for Infinite Variance

*Proceedings of the 39th International Conference on Machine Learning*, PMLR 162:1925-1944, 2022.

#### Abstract

In this paper, we extend the remarkable M-estimator of Catoni \citep{Cat12} to situations where the variance is infinite. In particular, given a sequence of i.i.d random variables $\{X_i\}_{i=1}^n$ from distribution $\mathcal{D}$ over $\mathbb{R}$ with mean $\mu$, we only assume the existence of a known upper bound $\upsilon_{\varepsilon} > 0$ on the $(1+\varepsilon)^{th}$ central moment of the random variables, namely, for $\varepsilon \in (0,1]$ \[ \mathbb{E}_{X_1 \sim \mathcal{D}} \Big| X_1 - \mu \Big|^{1+\varepsilon} \leq \upsilon_{\varepsilon}. \]{The} extension is non-trivial owing to the difficulty in characterizing the roots of certain polynomials of degree smaller than $2$. The proposed estimator has the same order of magnitude and the same asymptotic constant as in \citet{Cat12}, but for the case of bounded moments. We further propose a version of the estimator that does not require even the knowledge of $\upsilon_{\varepsilon}$, but adapts the moment bound in a data-driven manner. Finally, to illustrate the usefulness of the derived non-asymptotic confidence bounds, we consider an application in multi-armed bandits and propose best arm identification algorithms, in the fixed confidence setting, that outperform the state of the art.