Probabilistic Multi-task Learning

Probabilistic Multi-Task Learning is a multi-task learning framework that belongs to the class of probabilistic models by explicitly modeling task relationships and uncertainties through joint probability distributions over outputs and latent task structures.

35

Publications

1.8K

Citations

117

Authors

64

Institutions

Publications per year

2017–2026

15

Authors

117

  1. YZ
    Yu Zhang

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

    2 publications · 122 citations

    2

    h-index

  2. YY
    Yiming Yang

    Carnegie Mellon University, United States

    2 publications · 151 citations

    2

    h-index

  3. DY
    Dit‐Yan Yeung

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

    2 publications · 122 citations

    2

    h-index

  4. CS
    Carmen Sanchez

    University of Michigan, United States

    1 publications · 176 citations

    1

    h-index

  5. SD
    Suryalok Dash

    Indian Institute of Technology Bhubaneswar, India

    1 publications · 30 citations

    1

    h-index

1–5 of 117

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 132 citations

    4

    h-index

  2. 2

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 31 citations

    1

    h-index

  4. 1

    h-index

  5. Scientific Reports

    1 publications · 19 citations

    1

    h-index

1–5