A two-step framework combining block-wise imputation with shared and task-specific multi-task learning improves prediction under distribution and posterior shift.
In the Supplementary Material S.3, we conduct ablation experiments to demonstrate the individual roles of the two steps in our proposed method
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Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Data
A two-step framework combining block-wise imputation with shared and task-specific multi-task learning improves prediction under distribution and posterior shift.