Tamara Broderick

2.2k total citations
37 papers, 509 citations indexed

About

Tamara Broderick is a scholar working on Artificial Intelligence, Statistics and Probability and Occupational Therapy. According to data from OpenAlex, Tamara Broderick has authored 37 papers receiving a total of 509 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 14 papers in Statistics and Probability and 4 papers in Occupational Therapy. Recurrent topics in Tamara Broderick's work include Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Inference (13 papers) and Gaussian Processes and Bayesian Inference (13 papers). Tamara Broderick is often cited by papers focused on Bayesian Methods and Mixture Models (14 papers), Statistical Methods and Inference (13 papers) and Gaussian Processes and Bayesian Inference (13 papers). Tamara Broderick collaborates with scholars based in United States, Canada and United Kingdom. Tamara Broderick's co-authors include Michael I. Jordan, Jim Pitman, Trevor Campbell, U. Seljak, Rachel Mandelbaum, J. Brinkmann, Christopher M. Hirata, Ashia Wilson, Nicholas Boyd and Andre Wibisono and has published in prestigious journals such as Journal of the American Statistical Association, PLoS ONE and The Astrophysical Journal.

In The Last Decade

Tamara Broderick

35 papers receiving 478 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tamara Broderick United States	13	279	119	116	52	44	37	509
Małgorzata Bogdan Poland	17	126 0.5×	297 2.5×	320 2.8×	44 0.8×	41 0.9×	67	1.2k
Sang‐Yun Oh United States	8	56 0.2×	61 0.5×	308 2.7×	23 0.4×	36 0.8×	21	528
Chad Schafer United States	9	63 0.2×	54 0.5×	91 0.8×	26 0.5×	34 0.8×	25	242
Adolfo J. Quiróz Venezuela	11	151 0.5×	108 0.9×	10 0.1×	6 0.1×	80 1.8×	37	435
Nick Whiteley United Kingdom	13	270 1.0×	104 0.9×	4 0.0×	11 0.2×	57 1.3×	39	505
C. Donalek United States	13	82 0.3×	15 0.1×	854 7.4×	198 3.8×	158 3.6×	43	1.3k
Hantian Zhang Switzerland	9	153 0.5×	6 0.1×	52 0.4×	12 0.2×	119 2.7×	15	362
W. Oliveira Brazil	15	364 1.3×	42 0.4×	103 0.9×		27 0.6×	87	765
H. Junklewitz Germany	13	162 0.6×	5 0.0×	273 2.4×	14 0.3×	27 0.6×	25	571
Ani Thakar United States	10	83 0.3×	6 0.1×	55 0.5×	38 0.7×	54 1.2×	22	480

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-authorship network of co-authors of Tamara Broderick

This figure shows the co-authorship network connecting the top 25 collaborators of Tamara Broderick. A scholar is included among the top collaborators of Tamara Broderick 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 Tamara Broderick. Tamara Broderick is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Huggins, Jonathan H., et al.. (2023). Independent Finite Approximations for Bayesian Nonparametric Inference. Bayesian Analysis. 19(4). 1 indexed citations

Favaro, Stefano, et al.. (2022). Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation. Journal of the American Statistical Association. 119(545). 320–331. 2 indexed citations

Liu, Runjing, et al.. (2022). Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (with Discussion). Bayesian Analysis. 18(1). 4 indexed citations

Vertanen, Keith, et al.. (2022). A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. CHI Conference on Human Factors in Computing Systems. 1–17. 4 indexed citations

Stephenson, William & Tamara Broderick. (2020). Approximate Cross-Validation in High Dimensions with Guarantees. International Conference on Artificial Intelligence and Statistics. 2424–2434. 3 indexed citations

Cai, Diana, Trevor Campbell, & Tamara Broderick. (2020). Finite mixture models are typically inconsistent for the number of components. arXiv (Cornell University). 1 indexed citations

Huggins, Jonathan H., et al.. (2020). Validated Variational Inference via Practical Posterior Error Bounds. OpenBU (Boston University). 1792–1802. 4 indexed citations

Campbell, Trevor & Tamara Broderick. (2019). Automated Scalable Bayesian Inference via Hilbert Coresets. Journal of Machine Learning Research. 20(15). 1–38. 22 indexed citations

Huggins, Jonathan H., et al.. (2019). Practical Posterior Error Bounds from Variational Objectives. arXiv (Cornell University). 1 indexed citations

10.

Huggins, Jonathan H., et al.. (2019). The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. arXiv (Cornell University). 141–150. 1 indexed citations

11.

Broderick, Tamara, et al.. (2018). Covariances, Robustness, and Variational Bayes. Journal of Machine Learning Research. 19(51). 1–49. 14 indexed citations

12.

Liu, Runjing, et al.. (2018). Return of the Infinitesimal Jackknife. 1 indexed citations

13.

Huggins, Jonathan H., Ryan P. Adams, & Tamara Broderick. (2017). PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. DSpace@MIT (Massachusetts Institute of Technology). 30. 3612–3622. 3 indexed citations

14.

Huggins, Jonathan H., Trevor Campbell, & Tamara Broderick. (2016). Coresets for Scalable Bayesian Logistic Regression. DSpace@MIT (Massachusetts Institute of Technology). 29. 4080–4088. 11 indexed citations

15.

Broderick, Tamara, Michael I. Jordan, & Jim Pitman. (2012). Beta Processes, Stick-Breaking and Power Laws. Bayesian Analysis. 7(2). 41 indexed citations

16.

Broderick, Tamara & Robert B. Gramacy. (2011). Classification and Categorical Inputs with Treed Gaussian Process Models. Journal of Classification. 28(2). 244–270. 5 indexed citations

17.

Broderick, Tamara, KongFatt Wong‐Lin, & Philip Holmes. (2010). Closed-Form Approximations of First-Passage Distributions for a Stochastic Decision-Making Model. PubMed. 2009(2). 123–141. 10 indexed citations

18.

Broderick, Tamara & David Mackay. (2009). Fast and Flexible Selection with a Single Switch. PLoS ONE. 4(10). e7481–e7481. 16 indexed citations

19.

Mandelbaum, Rachel, Christopher M. Hirata, Tamara Broderick, U. Seljak, & J. Brinkmann. (2006). Ellipticity of dark matter haloes with galaxy-galaxy weak lensing. Monthly Notices of the Royal Astronomical Society. 370(2). 1008–1024. 80 indexed citations

20.

Huterer, Dragan, Alex Kim, Lawrence M. Krauss, & Tamara Broderick. (2004). Redshift Accuracy Requirements for Future Supernova and Number Count Surveys. The Astrophysical Journal. 615(2). 595–602. 29 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.

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