Rate-Loss Regions for Polynomial Regression with Side Information
<|reference_start|>Rate-Loss Regions for Polynomial Regression with Side Information: 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.<|reference_end|>
@article{wei2024rate-loss,
    title={Rate-Loss Regions for Polynomial Regression with Side Information},
    author={Jiahui Wei (INSA Rennes, UR, IMT Atlantique, IETR, Lab-STICC_CODES),
  Philippe Mary (INSA Rennes, UR, IETR), Elsa Dupraz (IMT Atlantique,
  Lab-STICC_CODES)},
    journal={International Zurich Seminar on Information and Communication
  (IZS), Mar 2024, Zurich, Switzerland},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06591},
    primaryClass={cs.IT math.IT}
}