Uniform error bounds for quantized dynamical models
Machine Learning
2026-02-18 v1 Machine Learning
Abstract
This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical contexts for system identification, and in particular hybrid system identification. Two families of bounds are obtained: slow-rate bounds via a block decomposition and fast-rate, variance-adaptive, bounds via a novel spaced-point strategy. The bounds scale with the number of bits required to encode the model and thus translate hardware constraints into interpretable statistical complexities.
Cite
@article{arxiv.2602.15586,
title = {Uniform error bounds for quantized dynamical models},
author = {Abdelkader Metakalard and Fabien Lauer and Kevin Colin and Marion Gilson},
journal= {arXiv preprint arXiv:2602.15586},
year = {2026}
}