Multi-output Gaussian Processes

Multi-Output Gaussian Processes is a Gaussian process model that extends the standard Gaussian process framework to jointly model multiple correlated output variables by defining a structured covariance function across both input domains and output dimensions.

73

Publications

3.6K

Citations

213

Authors

103

Institutions

Publications per year

2017–2026

55

Variants

Multi-output Gaussian Process

Authors

213

  1. MA
    Mauricio A. Álvarez

    Technological University of Pereira, Colombia

    7 publications · 608 citations

    7

    h-index

  2. ND
    Neil D. Lawrence

    University of Sheffield, United Kingdom

    4 publications · 538 citations

    4

    h-index

  3. JF
    Joseph Futoma

    Duke University, United States

    3 publications · 116 citations

    3

    h-index

  4. KH
    Katherine Heller

    Duke University, United States

    3 publications · 116 citations

    3

    h-index

  5. MS
    Mark Sendak

    Duke Institute for Health Innovation, United States

    3 publications · 116 citations

    3

    h-index

1–5 of 213

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 291 citations

    10

    h-index

  2. Knowledge-Based Systems

    4 publications · 394 citations

    4

    h-index

  3. Journal of Computational Physics

    3 publications · 376 citations

    3

    h-index

  4. 2

    h-index

  5. Neural Information Processing Systems

    Conference · 2 publications · 200 citations

    2

    h-index

1–5