Multiparameter regularization and aggregation in the context of polynomial functional regression
<|reference_start|>Multiparameter regularization and aggregation in the context of polynomial functional regression: Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results.<|reference_end|>
@article{gizewski2024multiparameter,
    title={Multiparameter regularization and aggregation in the context of
  polynomial functional regression},
    author={Elke R. Gizewski and Markus Holzleitner and Lukas Mayer-Suess and
  Sergiy Pereverzyev Jr. and Sergei V. Pereverzyev},
    journal={arXiv preprint arXiv:2405.04147},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04147},
    primaryClass={stat.ML cs.LG cs.NA math.NA math.ST stat.TH}
}