This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, compromising two crucial techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interactions; (ii) risk-guided group indicators, which exploit consistent task correlations with risk information from previous iterations. Comprehensive experimental results on diverse typical benchmarks demonstrate our method's performance superiority with even lower computational costs.

翻译：本文提出了一种名为\textit{GO4Align}的多任务优化方法，通过显式对齐各任务的优化过程来解决任务不平衡问题。为实现这一目标，我们设计了一种自适应组风险最小化策略，其中包含两项关键实施技术：(i) 动态组分配，基于任务交互将相似任务聚类；(ii) 风险引导组指标，利用先前迭代中的风险信息挖掘一致的任务关联性。在多个典型基准上的综合实验结果表明，本方法在性能上具有优势，且计算成本更低。