New transfer learning method achieves minimax-optimal scaling in regression

Researchers have developed a new method for transfer learning in high-dimensional regression, focusing on the performance improvements achievable through pretrained models. Their work establishes a deterministic equivalent for the risk of a two-step procedure involving a pretrained source model and a fine-tuned target model. The study identifies a specific regime where this approach achieves a minimax-optimal scaling law, with the optimal source regularization depending only on source data, suggesting universality across different target tasks. The findings are supported by experiments on synthetic data and language models, and the optimal rate can also be achieved using one-pass stochastic gradient descent for fine-tuning. AI

IMPACT This research could lead to more efficient and effective use of pretrained models in various AI applications, particularly in regression tasks.

RANK_REASON The cluster contains a research paper detailing a new method for transfer learning in regression. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• Hugging Face
• IArxiv
• L2
• stochastic gradient descent
• Tikhonov regularization
• transfer learning

• paper
• model release

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