AI RESEARCH
Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
arXiv CS.LG
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ArXi:2605.00473v1 Announce Type: new Multi-task learning (MTL) has emerged as a pivotal paradigm in machine learning by leveraging shared structures across multiple related tasks. Despite its empirical success, the development of likelihood-based efficiently solvable algorithms--even for shared linear representations--remains largely underdeveloped, primarily due to the non-convex structure intrinsic to matrix factorization. This paper