Adaptive and robust multi-task learning
提出一类自适应方法，能自动利用多个数据任务间的相似性并处理差异，同时具备对异常任务的稳健性，通过理论证明和实验验证了有效性。
We study the multitask learning problem that aims to simultaneously analyze multiple data sets 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 data sets demonstrate the efficacy of our new methods.