We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences. We derive sharp statistical guarantees for the methods and prove their robustness against outlier tasks. Numerical experiments on synthetic and real datasets demonstrate the efficacy of our new methods.

翻译：我们研究多任务学习问题，旨在同时分析来自不同来源的多个数据集，并为每个数据集学习一个模型。我们提出了一系列自适应方法，这些方法能自动利用任务间可能存在的相似性，同时谨慎处理它们的差异。我们为这些方法推导了严格的统计保证，并证明了它们对异常任务的稳健性。在合成数据集和真实数据集上进行的数值实验验证了我们新方法的有效性。