Rate-Loss Regions for Polynomial Regression with Side Information
Abstract
In the context of goal-oriented communications, this paper addresses the achievable rate versus generalization error region of a learning task applied on compressed data. The study focuses on the distributed setup where a source is compressed and transmitted through a noiseless channel to a receiver performing polynomial regression, aided by side information available at the decoder. The paper provides the asymptotic rate generalization error region, and extends the analysis to the non-asymptotic regime.Additionally, it investigates the asymptotic trade-off between polynomial regression and data reconstruction under communication constraints. The proposed achievable scheme is shown to achieve the minimum generalization error as well as the optimal rate-distortion region.
Cite
@article{arxiv.2407.06591,
title = {Rate-Loss Regions for Polynomial Regression with Side Information},
author = {Jiahui Wei and Philippe Mary and Elsa Dupraz},
journal= {arXiv preprint arXiv:2407.06591},
year = {2024}
}