Towards Principled Task Grouping for Multi-Task Learning
This paper presents a novel approach to task grouping in Multitask Learning (MTL), advancing beyond existing methods by addressing key theoretical and practical limitations. Unlike prior studies, our approach offers a more theoretically grounded method that does not rely on restrictive assumptions for constructing transfer gains. We also propose a flexible mathematical programming formulation which can accommodate a wide spectrum of resource constraints, thus enhancing its versatility. Experimental results across diverse domains, including computer vision datasets, combinatorial optimization benchmarks and time series tasks, demonstrate the superiority of our method over extensive baselines, validating its effectiveness and general applicability in MTL.
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