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Scalable efficient reproducible multi-task learning via data splitting

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  • Wen, Xin
  • Li, Yang
  • Zheng, Zemin

Abstract

In contemporary application, multi-task learning’s significance has surged. This paper presents a scalable, efficient variable selection method for reproducible multi-task learning through data splitting, offering theoretically guaranteed FDR control and exhibiting asymptotic power of one under mild assumptions.

Suggested Citation

  • Wen, Xin & Li, Yang & Zheng, Zemin, 2024. "Scalable efficient reproducible multi-task learning via data splitting," Statistics & Probability Letters, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:stapro:v:208:y:2024:i:c:s0167715224000403
    DOI: 10.1016/j.spl.2024.110071
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    References listed on IDEAS

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    1. Ethan Cohen‐Cole & Xiaodong Liu & Yves Zenou, 2018. "Multivariate choices and identification of social interactions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 165-178, March.
    2. Yingying Fan & Emre Demirkaya & Gaorong Li & Jinchi Lv, 2020. "RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 362-379, January.
    3. Emmanuel Candès & Yingying Fan & Lucas Janson & Jinchi Lv, 2018. "Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 551-577, June.
    4. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    5. Yanhang Zhang & Junxian Zhu & Jin Zhu & Xueqin Wang, 2023. "A Splicing Approach to Best Subset of Groups Selection," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 104-119, January.
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