# Multiparameter regularization and aggregation in the context of polynomial functional regression

arXiv: https://arxiv.org/abs/2405.04147 · HTML: https://mlsanity.com/paper/2405.04147/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distributed learning with regularized least squares](https://mlsanity.com/paper/1608.03339/) (2016)
- [Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation](https://mlsanity.com/paper/2305.01281/) (2023)
- [Domain Generalization by Functional Regression](https://mlsanity.com/paper/2302.04724/) (2023)
- [On regularized polynomial functional regression](https://mlsanity.com/paper/2311.03036/) (2023)

## Abstract (arXiv)

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.