# Robust multivariate mean estimation: the optimality of trimmed mean

## TL;DR

This paper introduces a multivariate trimmed-mean estimator for robust mean estimation under adversarial contamination, demonstrating its optimality with minimal assumptions.

## Contribution

It proposes a new multivariate trimmed-mean estimator and proves its optimality in robust mean estimation with adversarial noise.

## Key findings

- Estimator achieves optimal robustness bounds
- Performs well under minimal assumptions
- Outperforms existing methods in contaminated settings

## Abstract

We consider the problem of estimating the mean of a random vector based on i.i.d. observations and adversarial contamination. We introduce a multivariate extension of the trimmed-mean estimator and show its optimal performance under minimal conditions.