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Automatic Transfer Learning for high-dimensional linear regression

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  • Qu, Xinhao

Abstract

Transferability/Transportability has continuously been the central topic for transfer learning tasks, this paper designs Automatic Transfer Learning (ATL) that embeds such information within the learning process automatically. We demonstrate that, under high-dimensional linear setting, ATL estimator is doubly robust for negative transfer and achieves optimal rate under certain conditions. Numerical implementations also show its efficacy.

Suggested Citation

  • Qu, Xinhao, 2025. "Automatic Transfer Learning for high-dimensional linear regression," Statistics & Probability Letters, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:stapro:v:224:y:2025:i:c:s0167715225000902
    DOI: 10.1016/j.spl.2025.110445
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Ye Tian & Yang Feng, 2023. "Transfer Learning Under High-Dimensional Generalized Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2684-2697, October.
    3. Sai Li & T. Tony Cai & Hongzhe Li, 2022. "Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 149-173, February.
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