David M. Blei
Impact in
- General Social Sciences top 0.01%
- Computational and Text Analysis Methods
- Artificial Intelligence top 0.01%
- Topic Modeling
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Text and Document Classification Technologies
- Bayesian Methods and Mixture Models
Papers in
-
- Bayesian Methods and Mixture Models 68
- Topic Modeling 41
- Gaussian Processes and Bayesian Inference 34
- Natural Language Processing Techniques 23
- Advanced Text Analysis Techniques 12
-
- Statistical Methods and Inference 36
- Statistical Methods and Bayesian Inference 15
- Co-authors
- Michael I. Jordan (16 shared papers)Andrew Y. Ng (2 shared papers)John Lafferty (6 shared papers)Chong Wang (9 shared papers)Matthew J. Beal (2 shared papers)Yee Whye Teh (2 shared papers)Matthew D. Hoffman (10 shared papers)Jonathan Chang (5 shared papers)
- Journals
- Journal of Machine Learning Research (8 papers)Journal of the American Statistical Association (6 papers)The Annals of Applied Statistics (5 papers)Proceedings of the National Academy of Sciences (5 papers)Bayesian Analysis (4 papers)
- Partner nations
- United StatesCanadaFrance
In The Last Decade
David M. Blei
181 papers receiving 41.2k citations
David M. Blei's Hit Papers
Peers
Comparison fields: 5 of 224
- General Social Sciences 4.0k
- Artificial Intelligence 26.0k
- Statistical and Nonlinear Physics 5.4k
- Information Systems 9.5k
- Computer Vision and Pattern Recognition 7.2k
Countries citing papers authored by David M. Blei
This map shows the geographic impact of David M. Blei's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David M. Blei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David M. Blei more than expected).
Fields of papers citing papers by David M. Blei
This network shows the impact of papers produced by David M. Blei. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David M. Blei. The network helps show where David M. Blei may publish in the future.
Co-authors
The 25 scholars most cited alongside David M. Blei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 187 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Latent dirichlet allocation Hit paper breakdown → | 2003 | 18102 |
| 2 | Probabilistic topic models Hit paper breakdown → | 2012 | 3486 |
| 3 | Hierarchical Dirichlet Processes Hit paper breakdown → | 2006 | 2248 |
| 4 | Dynamic topic models Hit paper breakdown → | 2006 | 1502 |
| 5 | Reading Tea Leaves: How Humans Interpret Topic Models Hit paper breakdown → | 2009 | 1239 |
| 6 | Collaborative topic modeling for recommending scientific articles Hit paper breakdown → | 2011 | 1054 |
| 7 | Variational inference for Dirichlet process mixtures Hit paper breakdown → | 2006 | 895 |
| 8 | Mixed Membership Stochastic Blockmodels. Hit paper breakdown → | 2008 | 871 |
| 9 | Supervised Topic Models Hit paper breakdown → | 2010 | 857 |
| 10 | Online Learning for Latent Dirichlet Allocation Hit paper breakdown → | 2010 | 814 |
| 11 | Modeling annotated data Hit paper breakdown → | 2003 | 727 |
| 12 | Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding Hit paper breakdown → | 2013 | 689 |
| 13 | Stochastic variational inference Hit paper breakdown → | 2013 | 669 |
| 14 | Correlated Topic Models Hit paper breakdown → | 2005 | 611 |
| 15 | Hierarchical Topic Models and the Nested Chinese Restaurant Process Hit paper breakdown → | 2003 | 593 |
| 16 | Minimal Loss Hashing for Compact Binary Codes Hit paper breakdown → | 2011 | 467 |
| 17 | The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies Hit paper breakdown → | 2010 | 406 |
| 18 | Simultaneous image classification and annotation Hit paper breakdown → | 2009 | 397 |
| 19 | A correlated topic model of Science Hit paper breakdown → | 2018 | 386 |
| 20 | 2011 | 354 |
About David M. Blei
David M. Blei is a scholar working on Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, General Social Sciences and Statistical and Nonlinear Physics, having authored 187 papers that have together received 44.1k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (68 papers), Topic Modeling (41 papers), Statistical Methods and Inference (36 papers), Gaussian Processes and Bayesian Inference (34 papers), Natural Language Processing Techniques (23 papers), Computational and Text Analysis Methods (19 papers), Statistical Methods and Bayesian Inference (15 papers) and Advanced Text Analysis Techniques (12 papers). The work is most often cited by research in General Social Sciences (4.0k citations), Artificial Intelligence (26.0k citations), Statistical and Nonlinear Physics (5.4k citations), Information Systems (9.5k citations) and Computer Vision and Pattern Recognition (7.2k citations). David M. Blei has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Michael I. Jordan, Andrew Y. Ng, John Lafferty, Chong Wang, Matthew J. Beal, Yee Whye Teh, Matthew D. Hoffman, Jonathan Chang, Jon McAuliffe and Sean Gerrish. Their work appears in journals such as Journal of Machine Learning Research, Journal of the American Statistical Association, The Annals of Applied Statistics, Proceedings of the National Academy of Sciences and Bayesian Analysis.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.