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.

本研究提出一种新方法来进行多任务学习中的任务分组，弥补了现有方法的关键理论和实践限制。与以前的研究不同，我们的方法提供了一个更具理论基础的方法，不依赖于构建转移增益的限制性假设。同时，我们还提出了一种灵活的数学规划方法，可以适应广泛的资源约束，从而增强了其多样性。在计算机视觉数据集、组合优化基准和时间序列任务等多个领域的实验结果表明，我们的方法优于广泛的基线方法，验证了其在多任务学习中的有效性和普适性。