A Functional Extension of Multi-Output Learning
Abstract
When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers. We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.
Cite
Text
Lambert et al. "A Functional Extension of Multi-Output Learning." ICML 2019 Workshops: AMTL, 2019.Markdown
[Lambert et al. "A Functional Extension of Multi-Output Learning." ICML 2019 Workshops: AMTL, 2019.](https://mlanthology.org/icmlw/2019/lambert2019icmlw-functional/)BibTeX
@inproceedings{lambert2019icmlw-functional,
title = {{A Functional Extension of Multi-Output Learning}},
author = {Lambert, Alex and Brault, Romain and Szabo, Zoltan and d'Alche-Buc, Florence},
booktitle = {ICML 2019 Workshops: AMTL},
year = {2019},
url = {https://mlanthology.org/icmlw/2019/lambert2019icmlw-functional/}
}