Multi-task Gaussian Processes

Multi-Task Gaussian Processes is a probabilistic model that extends the standard Gaussian Process framework by enabling the joint modeling of multiple related tasks through shared latent functions or covariance structures, thereby capturing inter-task dependencies while maintaining task-specific characteristics.

62

Publications

4.6K

Citations

199

Authors

83

Institutions

Publications per year

2017–2026

31

Authors

199

  1. AG
    Andrew Gordon Wilson

    Cornell University, United States

    4 publications · 195 citations

    4

    h-index

  2. MA
    Marco A. F. Pimentel

    University of Oxford, United Kingdom

    3 publications · 366 citations

    3

    h-index

  3. DA
    David A. Clifton

    University of Oxford, United Kingdom

    3 publications · 366 citations

    3

    h-index

  4. MV
    Mihaela van der Schaar

    University of California, Los Angeles, United States

    3 publications · 181 citations

    3

    h-index

  5. TC
    Trevor Cohn

    The University of Melbourne, Australia

    3 publications · 153 citations

    3

    h-index

1–5 of 199

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 305 citations

    6

    h-index

  2. Neural Information Processing Systems

    Conference · 3 publications · 199 citations

    3

    h-index

  3. Max Planck Digital Library

    Repository · 1 publications · 55 citations

    1

    h-index

  4. Warwick Research Archive Portal (University of Warwick)

    Repository · 1 publications · 14 citations

    1

    h-index

  5. ERA

    Repository · 1 publications · 21 citations

    1

    h-index

1–5