Variational Density Estimation

Variational Density Estimation is a probabilistic density estimation method that formulates the approximation of an intractable data density as an optimization problem by minimizing the divergence between the true density and a parametrized family of approximate densities, typically using variational inference.

31

Publications

1.3K

Citations

92

Authors

46

Institutions

Publications per year

2017–2026

17

Authors

92

  1. CH
    Chin-Wei Huang

    Université de Montréal, Canada

    4 publications · 80 citations

    4

    h-index

  2. AC
    Aaron Courville

    Université de Montréal, Canada

    4 publications · 80 citations

    4

    h-index

  3. ST
    Shawn Tan

    National University of Singapore, Singapore

    2 publications · 35 citations

    2

    h-index

  4. RK
    Robert Kohn

    UNSW Sydney, Australia

    2 publications · 53 citations

    2

    h-index

  5. MJ
    Mykel J. Kochenderfer

    Stanford University, United States

    2 publications · 78 citations

    2

    h-index

1–5 of 92

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 538 citations

    10

    h-index

  2. Entropy

    2 publications · 65 citations

    2

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 53 citations

    2

    h-index

  4. Information Sciences

    1 publications · 79 citations

    1

    h-index

  5. 1

    h-index

1–5