Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Latent dirichlet allocation
200318.1k citationsDavid M. Blei, Andrew Y. Ng et al.Journal of Machine Learning Researchprofile →
2010814 citationsMatthew D. Hoffman, David M. Blei et al.profile →
Modeling annotated data
2003727 citationsDavid M. Blei, Michael I. Jordanprofile →
Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding
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).
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 of co-authors of David M. Blei
This figure shows the co-authorship network connecting the top 25 collaborators of David M. Blei.
A scholar is included among the top collaborators of David M. Blei based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with David M. Blei. David M. Blei is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tansey, Wesley, et al.. (2019). Relational Dose-Response Modeling for Cancer Drug Studies. arXiv (Cornell University).
4.
Tran, Dustin, Matthew D. Hoffman, Rif A. Saurous, et al.. (2017). Deep Probabilistic Programming. International Conference on Learning Representations.9 indexed citations
5.
Naesseth, Christian A., Francisco J. R. Ruiz, Scott W. Linderman, & David M. Blei. (2016). Rejection Sampling Variational Inference. arXiv (Cornell University).1 indexed citations
6.
Zhou, Mingyuan, et al.. (2016). Bayesian Poisson tucker decomposition for learning the structure of international relations. International Conference on Machine Learning. 2810–2819.9 indexed citations
7.
Ranganath, Rajesh, Adler Perotte, Noémie Elhadad, & David M. Blei. (2015). The survival filter: joint survival analysis with a latent time series. Uncertainty in Artificial Intelligence. 742–751.12 indexed citations
8.
Hoffman, Matthew D., David M. Blei, Chong Wang, & John Paisley. (2013). Stochastic variational inference. Journal of Machine Learning Research. 14(1). 1303–1347.669 indexed citations breakdown →
9.
Paisley, John, David M. Blei, & Michael I. Jordan. (2012). Stick-Breaking Beta Processes and the Poisson Process. International Conference on Artificial Intelligence and Statistics. 850–858.13 indexed citations
10.
Wang, Chong & David M. Blei. (2012). Truncation-free Online Variational Inference for Bayesian Nonparametric Models. Neural Information Processing Systems. 25. 413–421.24 indexed citations
11.
Mimno, David & David M. Blei. (2011). Bayesian Checking for Topic Models. Empirical Methods in Natural Language Processing. 227–237.37 indexed citations
Carin, Lawrence, David M. Blei, & John Paisley. (2011). Variational Inference for Stick-Breaking Beta Process Priors. International Conference on Machine Learning. 889–896.18 indexed citations
14.
Kostina, Victoria, et al.. (2010). Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net. International Conference on Artificial Intelligence and Statistics. 477–484.21 indexed citations
Wang, Chong & David M. Blei. (2009). Variational Inference for the Nested Chinese Restaurant Process. Neural Information Processing Systems. 22. 1990–1998.38 indexed citations
17.
Blei, David M., Thomas L. Griffiths, & Michael I. Jordan. (2007). The nested Chinese restaurant process and Bayesian inference of topic hierarchies. arXiv (Cornell University).1 indexed citations
18.
Blei, David M., et al.. (2007). Admixtures of latent blocks with application to protein interaction networks. arXiv (Cornell University).1 indexed citations
19.
Blei, David M., Andrew Y. Ng, & Michael I. Jordan. (2003). Latent dirichlet allocation. Journal of Machine Learning Research. 3. 993–1022.18102 indexed citations breakdown →
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.