Scalable efficient reproducible multi-task learning via data splitting

In contemporary application, multi -task learning's significance has surged. This paper presents a scalable, efficient variable selection method for reproducible multi -task learning through data splitting, offering theoretically guaranteed FDR control and exhibiting asymptotic power of one under mild assumptions.