Adaptive and Robust Multi-Task Learning
Machine Learning
2023-09-19 v4 Machine Learning
Statistics Theory
Methodology
Statistics Theory
Abstract
We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences. We derive sharp statistical guarantees for the methods and prove their robustness against outlier tasks. Numerical experiments on synthetic and real datasets demonstrate the efficacy of our new methods.
Cite
@article{arxiv.2202.05250,
title = {Adaptive and Robust Multi-Task Learning},
author = {Yaqi Duan and Kaizheng Wang},
journal= {arXiv preprint arXiv:2202.05250},
year = {2023}
}
Comments
72 pages, 2 figures