On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note
Probability
2026-05-28 v1 Artificial Intelligence
Machine Learning
Abstract
This short note presents a dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear mappings. Discovered by Gemini 3.5 Flash, this result applies to any bounded function under a well-conditioned covariance. We apply this tool to answer a question of Simone Bombari on sign-quantized linear maps .
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Cite
@article{arxiv.2605.27563,
title = {On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note},
author = {Guangyi Zou and Roman Vershynin},
journal= {arXiv preprint arXiv:2605.27563},
year = {2026}
}
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4 pages