Tamara Broderick

2.2k citations
37 papers · 509 · h-index 13

Impact in

Papers in

    • Bayesian Methods and Mixture Models 14
    • Gaussian Processes and Bayesian Inference 13
    • Machine Learning and Algorithms 3
    • Explainable Artificial Intelligence (XAI) 3
    • Statistical Methods and Inference 13
    • Statistical Methods and Bayesian Inference 4

Tamara Broderick

35 papers receiving 478 citations

Peers

Tamara Broderick
Comparison fields: 5 of 72
  • Instrumentation 52
  • Statistics and Probability 119
  • Artificial Intelligence 279
  • Astronomy and Astrophysics 116
  • Statistical and Nonlinear Physics 33
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Małgorzata Bogdan Poland
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Citations per field
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Citations per year

Countries citing papers authored by Tamara Broderick

Since Specialization
Citations

This map shows the geographic impact of Tamara Broderick'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 Tamara Broderick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamara Broderick more than expected).

Fields of papers citing papers by Tamara Broderick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tamara Broderick. 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 Tamara Broderick. The network helps show where Tamara Broderick may publish in the future.

Co-authors

The 25 scholars most cited alongside Tamara Broderick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tamara Broderick Line = papers co-authored together Tamara Broderick links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200680
2 201365
3 201241
4 201333
5 200429
6 201329
7 201228
8 201427
9
Automated Scalable Bayesian Inference via Hilbert Coresets
201922
10 200916
11 201316
12 201814
13 201514
14 201312
15
Coresets for Scalable Bayesian Logistic Regression
201611
16 201211
17 201010
18 20188
19 20216
20 20115

About Tamara Broderick

Tamara Broderick is a scholar working on Artificial Intelligence, Statistics and Probability, Occupational Therapy, Computational Theory and Mathematics and Molecular Biology, having authored 37 papers that have together received 509 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Inference (13 papers), Gaussian Processes and Bayesian Inference (13 papers), Statistical Methods and Bayesian Inference (4 papers), Assistive Technology in Communication and Mobility (4 papers), Gaze Tracking and Assistive Technology (3 papers), Machine Learning and Algorithms (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). The work is most often cited by research in Instrumentation (52 citations), Statistics and Probability (119 citations), Artificial Intelligence (279 citations), Astronomy and Astrophysics (116 citations) and Statistical and Nonlinear Physics (33 citations). Tamara Broderick has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Michael I. Jordan, Jim Pitman, Trevor Campbell, J. Brinkmann, Christopher M. Hirata, Rachel Mandelbaum, U. Seljak, Ashia Wilson, Nicholas Boyd and Andre Wibisono. Their work appears in journals such as Bayesian Analysis, The Astrophysical Journal, Journal of Machine Learning Research, Statistical Science and Journal of the American Statistical Association.

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.

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