Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Data
This work addresses a specific challenge in multi-task learning for domains like healthcare and biomedical research, but it is incremental as it builds on existing methods to handle multiple forms of heterogeneity in a unified framework.
The paper tackles the problem of multi-task learning with heterogeneous multi-source block-wise missing data by proposing a two-step strategy that imputes missing blocks and disentangles shared and task-specific components, achieving superior performance in numerical experiments and real-data analysis from the ADNI database.
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.