1.4k total citations 10 papers, 379 citations indexed
About
James Jordon is a scholar working on Artificial Intelligence, Statistics and Probability and Surgery.
According to data from OpenAlex, James Jordon has authored 10 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Statistics and Probability and 1 paper in Surgery. Recurrent topics in James Jordon's work include Privacy-Preserving Technologies in Data (3 papers), Neural Networks and Applications (2 papers) and Machine Learning in Healthcare (2 papers). James Jordon is often cited by papers focused on Privacy-Preserving Technologies in Data (3 papers), Neural Networks and Applications (2 papers) and Machine Learning in Healthcare (2 papers). James Jordon collaborates with scholars based in United Kingdom, United States and Belgium. James Jordon's co-authors include Mihaela van der Schaar, Jinsung Yoon, Yao Zhang, Onur Atan, Ioana Bica, Ahmed M. Alaa, Daniel Jarrett, Ari Ercole, Danielle Belgrave and Paul Elbers and has published in prestigious journals such as Pure Amsterdam UMC, arXiv (Cornell University) and Lirias (KU Leuven).
In The Last Decade
James Jordon
10 papers
receiving
360 citations
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of James Jordon'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 James Jordon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Jordon more than expected).
This network shows the impact of papers produced by James Jordon. 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 James Jordon. The network helps show where James Jordon may publish in the future.
Co-authorship network of co-authors of James Jordon
This figure shows the co-authorship network connecting the top 25 collaborators of James Jordon.
A scholar is included among the top collaborators of James Jordon 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 James Jordon. James Jordon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
10 of 10 papers shown
1.
Jordon, James, et al.. (2020). OrganITE: Optimal transplant donor organ offering using an individual treatment effect. Lirias (KU Leuven). 33. 20037–20050.5 indexed citations
2.
Yoon, Jinsung, Yao Zhang, James Jordon, & Mihaela van der Schaar. (2020). VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. Neural Information Processing Systems. 33. 11033–11043.64 indexed citations
3.
Jordon, James, Daniel Jarrett, Jinsung Yoon, et al.. (2020). Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. Pure Amsterdam UMC. 206–215.7 indexed citations
Jordon, James, Jinsung Yoon, & Mihaela van der Schaar. (2019). Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. Neural Information Processing Systems. 32. 4323–4332.4 indexed citations
6.
Jordon, James, Jinsung Yoon, & Mihaela van der Schaar. (2018). KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks. International Conference on Learning Representations.19 indexed citations
7.
Yoon, Jinsung, James Jordon, & Mihaela van der Schaar. (2018). INVASE: Instance-wise Variable Selection using Neural Networks. International Conference on Learning Representations.38 indexed citations
8.
Jordon, James, Jinsung Yoon, & Mihaela van der Schaar. (2018). PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. International Conference on Learning Representations.145 indexed citations
9.
Yoon, Jinsung, James Jordon, & Mihaela van der Schaar. (2018). GAIN: Missing Data Imputation using Generative Adversarial Nets. International Conference on Machine Learning. 5689–5698.61 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.