41
2

Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Data

Main:31 Pages
9 Figures
Bibliography:1 Pages
2 Tables
Appendix:15 Pages
Abstract

Multi-task learning (MTL) has emerged as an imperative machine learning tool to solve multiple learning tasks simultaneously and has been successfully applied to healthcare, marketing, and biomedical fields. However, in order to borrow information across different tasks effectively, it is essential to utilize both homogeneous and heterogeneous information. Among the extensive literature on MTL, various forms of heterogeneity are presented in MTL problems, such as block-wise, distribution, and posterior heterogeneity. Existing methods, however, struggle to tackle these forms of heterogeneity simultaneously in a unified framework. In this paper, we propose a two-step learning strategy for MTL which addresses the aforementioned heterogeneity. First, we impute the missing blocks using shared representations extracted from homogeneous source across different tasks. Next, we disentangle the mappings between input features and responses into a shared component and a task-specific component, respectively, thereby enabling information borrowing through the shared component. Our numerical experiments and real-data analysis from the ADNI database demonstrate the superior MTL performance of the proposed method compared to other competing methods.

View on arXiv
@article{sui2025_2505.24413,
  title={ Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Data },
  author={ Yang Sui and Qi Xu and Yang Bai and Annie Qu },
  journal={arXiv preprint arXiv:2505.24413},
  year={ 2025 }
}
Comments on this paper