Variational Gaussian Processes

Variational Gaussian Processes is a variational inference framework that approximates the posterior distribution in Gaussian process models by optimizing a tractable variational distribution over latent variables, thereby combining the flexibility of Gaussian processes with scalable Bayesian inference.

88

Publications

5.2K

Citations

245

Authors

114

Institutions

Publications per year

2017–2026

47

Authors

245

  1. JH
    James Hensman

    University of Sheffield, United Kingdom

    6 publications · 866 citations

    6

    h-index

  2. ND
    Neil D. Lawrence

    University of Sheffield, United Kingdom

    5 publications · 613 citations

    5

    h-index

  3. CE
    Carl Edward Rasmussen

    University of Cambridge, United Kingdom

    5 publications · 317 citations

    5

    h-index

  4. BB
    Byron Boots

    Georgia Institute of Technology, United States

    4 publications · 107 citations

    4

    h-index

  5. CC
    Ching-An Cheng

    Georgia Institute of Technology, United States

    4 publications · 107 citations

    4

    h-index

1–5 of 245

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 1.3K citations

    15

    h-index

  2. 3

    h-index

  3. Neural Information Processing Systems

    Conference · 3 publications · 93 citations

    3

    h-index

  4. Engineering Structures

    2 publications · 112 citations

    2

    h-index

  5. Neural Computation

    2 publications · 400 citations

    2

    h-index

1–5