Indian Buffet Process

The Indian Buffet Process is a Bayesian nonparametric stochastic process that generates sparse binary matrices with infinitely many columns, used to model latent feature assignments in which each data point can exhibit a subset of an unbounded number of features.

70

Publications

4.5K

Citations

166

Authors

80

Institutions

Publications per year

2017–2026

16

Authors

166

  1. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    13 publications · 1.7K citations

    13

    h-index

  2. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    8 publications · 774 citations

    8

    h-index

  3. YW
    Yee Whye Teh

    University of Oxford, United Kingdom

    5 publications · 499 citations

    5

    h-index

  4. TL
    Thomas L. Griffiths

    University of California, Berkeley, United States

    5 publications · 968 citations

    5

    h-index

  5. FD
    Finale Doshi‐Velez

    Harvard University Press, United States

    4 publications · 115 citations

    4

    h-index

1–5 of 166

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 280 citations

    10

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  2. The Annals of Applied Statistics

    AOAS · 4 publications · 283 citations

    4

    h-index

  3. Neural Information Processing Systems

    Conference · 2 publications · 29 citations

    2

    h-index

  4. Cambridge University Engineering Department Publications Database

    Repository · 2 publications · 190 citations

    2

    h-index

  5. 2

    h-index

1–5