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 →
Machine learning: Trends, perspectives, and prospects
20156.0k citationsMichael I. Jordan et al.profile →
Countries citing papers authored by Michael I. Jordan
Since
Specialization
Citations
This map shows the geographic impact of Michael I. Jordan'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 Michael I. Jordan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael I. Jordan more than expected).
Fields of papers citing papers by Michael I. Jordan
This network shows the impact of papers produced by Michael I. Jordan. 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 Michael I. Jordan. The network helps show where Michael I. Jordan may publish in the future.
Co-authorship network of co-authors of Michael I. Jordan
This figure shows the co-authorship network connecting the top 25 collaborators of Michael I. Jordan.
A scholar is included among the top collaborators of Michael I. Jordan 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 Michael I. Jordan. Michael I. Jordan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ho, Nhat, et al.. (2020). Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter.. International Conference on Artificial Intelligence and Statistics. 2088–2097.1 indexed citations
Cheng, Xiang, Niladri S. Chatterji, Peter L. Bartlett, & Michael I. Jordan. (2017). Underdamped Langevin MCMC: A non-asymptotic analysis. Conference on Learning Theory. 300–323.9 indexed citations
6.
Chen, Jianbo, Mitchell Stern, Martin J. Wainwright, & Michael I. Jordan. (2017). Kernel feature selection via conditional covariance minimization. Neural Information Processing Systems. 30. 6949–6958.7 indexed citations
7.
Kleiner, Ariel, Ameet Talwalkar, Purnamrita Sarkar, & Michael I. Jordan. (2014). A Scalable Bootstrap for Massive Data. Journal of the Royal Statistical Society Series B (Statistical Methodology). 76(4). 795–816.218 indexed citations
Kulis, Brian, et al.. (2012). Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. Neural Information Processing Systems. 25. 3158–3166.28 indexed citations
Bouchard‐Côté, Alexandre & Michael I. Jordan. (2010). Variational Inference over Combinatorial Spaces. Neural Information Processing Systems. 23. 280–288.6 indexed citations
12.
Duchi, John C., Lester Mackey, & Michael I. Jordan. (2010). On the Consistency of Ranking Algorithms. UC Berkeley. 327–334.51 indexed citations
13.
Sutton, Charles & Michael I. Jordan. (2008). Probabilistic inference in queueing networks. Edinburgh Research Explorer (University of Edinburgh). 6–6.4 indexed citations
14.
Wainwright, Martin J. & Michael I. Jordan. (2007). Graphical Models, Exponential Families, and Variational Inference. now publishers, Inc. eBooks.1562 indexed citations breakdown →
15.
Flaherty, Patrick, Adam P. Arkin, & Michael I. Jordan. (2005). Robust design of biological experiments. Neural Information Processing Systems. 18. 363–370.49 indexed citations
16.
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 →
17.
Ghaoui, Laurent El, Michael I. Jordan, & Gert Lanckriet. (2002). Robust Novelty Detection with Single-Class MPM. Neural Information Processing Systems. 15. 929–936.58 indexed citations
18.
Bach, Francis & Michael I. Jordan. (2002). Learning Graphical Models with Mercer Kernels. Neural Information Processing Systems. 15. 1033–1040.25 indexed citations
19.
Freitas, Nando de, et al.. (2001). Variational MCMC. Uncertainty in Artificial Intelligence. 120–127.34 indexed citations
20.
Jordan, Michael I.. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. eScholarship (California Digital Library). 112–127.182 indexed citations
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.