Stochastic Variational Inference

Stochastic Variational Inference is a variational inference method that employs stochastic optimization to scale Bayesian posterior approximation to large datasets by using noisy gradients estimated from mini-batches of data.

144

Publications

29.2K

Citations

366

Authors

146

Institutions

Publications per year

2017–2026

87

Authors

366

  1. DM
    David M. Blei

    Princeton University, United States

    13 publications · 3K citations

    12

    h-index

  2. JP
    John Paisley

    Columbia University, United States

    7 publications · 1.8K citations

    7

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    6 publications · 1.1K citations

    6

    h-index

  4. DD
    David Duvenaud

    University of Toronto, Canada

    5 publications · 583 citations

    5

    h-index

  5. CW
    Chong Wang

    Tsinghua University, China

    5 publications · 1.6K citations

    5

    h-index

1–5 of 366

Venues

  1. arXiv (Cornell University)

    Repository · 45 publications · 3.7K citations

    27

    h-index

  2. Neural Information Processing Systems

    Conference · 7 publications · 492 citations

    7

    h-index

  3. International Conference on Artificial Intelligence and Statistics

    Conference · 4 publications · 91 citations

    4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 126 citations

    4

    h-index

  5. UvA-DARE (University of Amsterdam)

    Repository · 3 publications · 17.8K citations

    3

    h-index

1–5