David M. Blei
- General Social Sciences top 0.01%
- Computational and Text Analysis Methods 19
- Artificial Intelligence top 0.01%
- 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 and Nonlinear Physics top 0.05%
- Information Systems top 0.01%
- Computer Vision and Pattern Recognition top 0.05%
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- Statistical Methods and Inference 36
- Statistical Methods and Bayesian Inference 15
- Co-authors
- Michael I. JordanAndrew Y. NgJohn LaffertyChong WangYee Whye TehMatthew J. BealMatthew D. HoffmanJonathan Chang
- Journals
- Journal of Machine Learning Research (8 papers)Journal of the American Statistical Association (6 papers)The Annals of Applied Statistics (5 papers)
- Partner nations
- United StatesCanadaFrance
In The Last Decade
David M. Blei
181 papers receiving 41.2k citations
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | Adapting Text Embeddings for Causal Inference | 2020 | 6 |
| 2 | 2019 | 43 | |
| 3 | 2017 | 157 | |
| 4 | Deep and Hierarchical Implicit Models. | 2017 | 20 |
| 5 | Bayesian Poisson tucker decomposition for learning the structure of international relations | 2016 | 9 |
| 6 | Stochastic Structured Variational Inference | 2014 | 11 |
| 7 | The Inverse Regression Topic Model | 2014 | 24 |
| 8 | Stick-Breaking Beta Processes and the Poisson Process | 2012 | 13 |
| 9 | Online Variational Inference for the Hierarchical Dirichlet Process | 2011 | 194 |
| 10 | Bayesian Checking for Topic Models | 2011 | 37 |
| 11 | Predicting Legislative Roll Calls from Text | 2011 | 102 |
| 12 | Spatial distance dependent Chinese restaurant processes for image segmentation | 2011 | 40 |
| 13 | Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net | 2010 | 21 |
| 14 | Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable | 2010 | 21 |
| 15 | The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling | 2010 | 80 |
| 16 | Bayesian Nonparametric Matrix Factorization for Recorded Music | 2010 | 88 |
| 17 | Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process | 2009 | 83 |
| 18 | Reading Tea Leaves: How Humans Interpret Topic Modelsbreakdown → | 2009 | 1239 |
| 19 | Relational Topic Models for Document Networks | 2009 | 322 |
| 20 | Correlated Topic Modelsbreakdown → | 2005 | 611 |
About David M. Blei
David M. Blei is a scholar working on Statistics and Probability, General Social Sciences and Artificial Intelligence, 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) and Statistical and Nonlinear Physics (5.4k 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, Yee Whye Teh, Matthew J. Beal, 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.