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arXiv Machine Learning By Xiaodong Li, Zhentao Li

Multitask Regression with Pairwise Fusion

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The paper investigates multitask regression where different predictors may have varying degrees of coefficient sharing across tasks. It introduces a framework that quantifies sharing by the number of active predictors and the total number of task-specific coefficient deviations, and proposes a pairwise penalty estimator that achieves matching upper and lower bounds in terms of these quantities. The method also handles scenarios where a large subset of tasks shares an identical coefficient vector, with explicit sample‑size conditions ensuring exact pooling of those tasks while allowing others to differ.

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