Towards Principled Task Grouping for Multi-Task Learning
Chenguang Wang Xuanhao Pan Tianshu Yu
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.