Task Relatedness Modeling

Task Relatedness Modeling is a computational framework that characterizes the interdependencies among multiple learning tasks by identifying shared structures, statistical relationships, or inductive biases that distinguish it from general multi-task learning approaches.

129

Publications

10.2K

Citations

357

Authors

155

Institutions

Publications per year

2017–2026

58

Authors

357

  1. YZ
    Yu Zhang

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

    6 publications · 762 citations

    6

    h-index

  2. HD
    Hal Daumé

    University of Maryland, College Park, United States

    4 publications · 236 citations

    4

    h-index

  3. AS
    Anders Søgaard

    University of Copenhagen, Denmark

    4 publications · 398 citations

    4

    h-index

  4. SV
    Stefan Vlaski

    École Polytechnique Fédérale de Lausanne, Switzerland

    4 publications · 173 citations

    4

    h-index

  5. JB
    Joachim Bingel

    University of Copenhagen, Denmark

    4 publications · 398 citations

    4

    h-index

1–5 of 357

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 886 citations

    12

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 288 citations

    9

    h-index

  3. 6

    h-index

  4. IEEE Transactions on Image Processing

    3 publications · 151 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 92 citations

    3

    h-index

1–5