Low-rank Multi-task Learning

Low-Rank Multi-Task Learning is a multi-task learning framework that leverages a low-rank assumption on the parameter space to model task relatedness by capturing shared structures among tasks through a compressed, lower-dimensional representation.

39

Publications

5.2K

Citations

129

Authors

76

Institutions

Publications per year

2017–2026

22

Authors

129

  1. JZ
    Jiayu Zhou

    Michigan State University, United States

    4 publications · 425 citations

    4

    h-index

  2. JY
    Jieping Ye

    Arizona State University, United States

    4 publications · 586 citations

    4

    h-index

  3. YZ
    Yu Zhang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 2.9K citations

    3

    h-index

  4. WG
    Wen Gao

    Peking University, China

    2 publications · 321 citations

    2

    h-index

  5. QT
    Qi Tian

    The University of Texas at San Antonio, United States

    2 publications · 321 citations

    2

    h-index

1–5 of 129

Venues

  1. Remote Sensing

    4 publications · 128 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 670 citations

    3

    h-index

  3. 2

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 2.2K citations

    2

    h-index

  5. Computational Intelligence and Neuroscience

    1 publications · 12 citations

    1

    h-index

1–5