Robust Mean Estimation under Quantization
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.
Fields of Study & Subjects
Advanced Statistical Methods and Models
Distributed Sensor Networks and Detection Algorithms
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 11 January 2026
- Document type
- Preprint