Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
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 introduces a first-order algorithm that jointly learns a shared representation and task-specific parameters, with guaranteed efficiency. Notably, it converges in $\widetilde{\mathcal{O}}(1)$ iterations and attains a \emph{near-optimal} estimation error of $\widetilde{\mathcal{O}}(dk/(TN))$, \e...
| Authors | Shihong Ding, Fangyu Du, Cong Fang |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 6 May 2026 |
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