Robust and Efficient Multitask Learning via Sparse Heterogeneity

The core message of this paper is to propose a novel two-stage robust multitask learning estimator, RMEstimator, that efficiently exploits sparse heterogeneity across related learning tasks to obtain improved sample complexity bounds, especially for "data-poor" tasks.