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
70
Hierarchical Beta Processes and the Indian Buffet Process
Romain Thibaux, Michael I. Jordan · UC Berkeley · 2007 · 354 citations
Stick-breaking Construction for the Indian Buffet Process
Yee Whye Teh, Dilan Görür, Zoubin Ghahramani · 2007 · 203 citations
1–5 of 70
166
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80
University of Cambridge
Cambridge, United Kingdom
11 h-index
22 publications
1.4K citations
University of California, Berkeley
Berkeley, United States
9 h-index
20 publications
1.1K citations
University of Chicago
Chicago, United States
5 h-index
5 publications
218 citations
Columbia University
New York, United States
4 h-index
4 publications
72 citations
University of Houston
Houston, United States
3 h-index
8 publications
718 citations

Universidad Carlos III de Madrid
Madrid, Spain
3 h-index
7 publications
124 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
5 publications
71 citations
Duke University
Durham, United States
3 h-index
17 publications
798 citations
1–8 of 80
Repository · 10 publications · 280 citations
10
h-index
AOAS · 4 publications · 283 citations
4
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Conference · 2 publications · 29 citations
2
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Repository · 2 publications · 190 citations
2
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2 publications · 149 citations
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