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.

翻译：本文提出了一种多任务学习（MTL）中任务分组的新方法，通过克服现有方法在理论和实践上的关键局限性实现了超越。与先前研究不同，我们的方法提供了更具理论基础的方案，无需依赖构建迁移增益时的限制性假设。我们还提出了一种灵活的数学规划公式，可适应广泛的资源约束条件，从而增强其通用性。在包括计算机视觉数据集、组合优化基准测试和时间序列任务等多个领域的实验结果表明，我们的方法在广泛基线上展现出优越性，验证了其在多任务学习中的有效性和普遍适用性。