Robust Mean Estimation under Quantization
Machine Learning
2026-01-13 v1 Machine Learning
Statistics Theory
Statistics Theory
Abstract
We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a single sample, and the second is a partial quantization setting, in which the estimator may use a small fraction of unquantized data.
Cite
@article{arxiv.2601.07074,
title = {Robust Mean Estimation under Quantization},
author = {Pedro Abdalla and Junren Chen},
journal= {arXiv preprint arXiv:2601.07074},
year = {2026}
}