# Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study

arXiv: https://arxiv.org/abs/2502.11324 · HTML: https://mlsanity.com/paper/2502.11324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2025.

## Abstract (arXiv)

Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the high-dimensional setting.