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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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