Gregory Plumb
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Imbalanced Data Classification Techniques
Papers in
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- Machine Learning and Data Classification 4
- Explainable Artificial Intelligence (XAI) 4
- Adversarial Robustness in Machine Learning 2
- Data Stream Mining Techniques 1
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- Advanced Data Compression Techniques 1
- Co-authors
- Ameet Talwalkar (4 shared papers)Jeffrey Li (2 shared papers)Valerie Chen (3 shared papers)Joon Sik Kim (2 shared papers)Risi Kondor (1 shared paper)Vikas Singh (1 shared paper)Ángel Alexander Cabrera (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Queue (1 paper)Communications of the ACM (1 paper)Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Gregory Plumb
6 papers receiving 484 citations
Gregory Plumb's Hit Papers
Peers
Comparison fields: 5 of 128
- Health Informatics 18
- Artificial Intelligence 237
- Management Science and Operations Research 37
- Safety Research 24
- Health Information Management 13
Countries citing papers authored by Gregory Plumb
This map shows the geographic impact of Gregory Plumb'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 Gregory Plumb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregory Plumb more than expected).
Fields of papers citing papers by Gregory Plumb
This network shows the impact of papers produced by Gregory Plumb. 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 Gregory Plumb. The network helps show where Gregory Plumb may publish in the future.
Co-authors
The 7 scholars most cited alongside Gregory Plumb, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Interpretable Machine Learning Hit paper breakdown → | 2021 | 367 |
| 2 | Interpretable machine learning Hit paper breakdown → | 2022 | 138 |
| 3 | 2023 | 5 | |
| 4 | S n FFT: a Julia toolkit for Fourier analysis of functions over permutations | 2015 | 3 |
| 5 | Supervised Local Modeling for Interpretability. | 2018 | 1 |
| 6 | 2022 | 1 |
About Gregory Plumb
Gregory Plumb is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mathematics, Signal Processing and Computer Science Applications, having authored 6 papers that have together received 515 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Explainable Artificial Intelligence (XAI) (4 papers), Adversarial Robustness in Machine Learning (2 papers), Data Stream Mining Techniques (1 paper), Blind Source Separation Techniques (1 paper), Tensor decomposition and applications (1 paper), Advanced Data Compression Techniques (1 paper) and Mobile Crowdsensing and Crowdsourcing (1 paper). The work is most often cited by research in Health Informatics (18 citations), Artificial Intelligence (237 citations), Management Science and Operations Research (37 citations), Safety Research (24 citations) and Health Information Management (13 citations). Gregory Plumb has collaborated with scholars based in United States. Frequent co-authors include Ameet Talwalkar, Jeffrey Li, Valerie Chen, Joon Sik Kim, Risi Kondor, Vikas Singh and Ángel Alexander Cabrera. Their work appears in journals such as Journal of Machine Learning Research, Queue, Communications of the ACM, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing and arXiv (Cornell University).
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.