AI RESEARCH
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
arXiv CS.LG
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ArXi:2603.28739v1 Announce Type: new In transfer learning, the learner leverages auxiliary data to improve generalization on a main task. However, the precise theoretical understanding of when and how auxiliary data help remains incomplete. We provide new insights on this issue in two canonical linear settings: ordinary least squares regression and under-parameterized linear neural networks.