James Robert Lloyd
Impact in
- Artificial Intelligence top 10%
- Gaussian Processes and Bayesian Inference
- Bayesian Methods and Mixture Models
- Machine Learning and Data Classification
- Data Stream Mining Techniques
Papers in
-
- Gaussian Processes and Bayesian Inference 4
- Machine Learning and Data Classification 2
- Advanced Graph Neural Networks 1
- Machine Learning and Algorithms 1
- Bayesian Modeling and Causal Inference 1
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- Time Series Analysis and Forecasting 2
- Co-authors
- Zoubin Ghahramani (3 shared papers)Joshua B. Tenenbaum (1 shared paper)Roger Grosse (1 shared paper)David Duvenaud (1 shared paper)Peter Orbanz (1 shared paper)Daniel M. Roy (1 shared paper)Tomoharu Iwata (1 shared paper)Zoubin Ghahramani (1 shared paper)
- Journals
- International Journal of Forecasting (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)SPIRE - Sciences Po Institutional REpository (1 paper)LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United KingdomUnited StatesJapan
In The Last Decade
James Robert Lloyd
7 papers receiving 245 citations
Peers
Comparison fields: 5 of 70
- Computational Mathematics 3
- Artificial Intelligence 133
- Statistics and Probability 31
- Statistical and Nonlinear Physics 38
- Management Science and Operations Research 36
Countries citing papers authored by James Robert Lloyd
This map shows the geographic impact of James Robert Lloyd'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 Robert Lloyd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Robert Lloyd more than expected).
Fields of papers citing papers by James Robert Lloyd
This network shows the impact of papers produced by James Robert Lloyd. 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 Robert Lloyd. The network helps show where James Robert Lloyd may publish in the future.
Co-authors
The 19 scholars most cited alongside James Robert Lloyd, 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 | 2013 | 78 | |
| 2 | 2014 | 69 | |
| 3 | Random function priors for exchangeable arrays with applications to graphs and relational data | 2012 | 44 |
| 4 | A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention | 2016 | 28 |
| 5 | Statistical model criticism using kernel two sample tests | 2015 | 26 |
| 6 | 2015 | 8 | |
| 7 | 2013 | 5 |
About James Robert Lloyd
James Robert Lloyd is a scholar working on Artificial Intelligence, Signal Processing, Information Systems, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 7 papers that have together received 258 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (4 papers), Machine Learning and Data Classification (2 papers), Time Series Analysis and Forecasting (2 papers), Advanced Graph Neural Networks (1 paper), Machine Learning and Algorithms (1 paper), Bayesian Modeling and Causal Inference (1 paper), Recommender Systems and Techniques (1 paper) and Model Reduction and Neural Networks (1 paper). The work is most often cited by research in Computational Mathematics (3 citations), Artificial Intelligence (133 citations), Statistics and Probability (31 citations), Statistical and Nonlinear Physics (38 citations) and Management Science and Operations Research (36 citations). James Robert Lloyd has collaborated with scholars based in United Kingdom, United States and Japan. Frequent co-authors include Zoubin Ghahramani, Joshua B. Tenenbaum, Roger Grosse, David Duvenaud, Peter Orbanz, Daniel M. Roy, Tomoharu Iwata, Zoubin Ghahramani, Núria Macià and Sérgio Escalera. Their work appears in journals such as International Journal of Forecasting, IEEE Transactions on Pattern Analysis and Machine Intelligence, SPIRE - Sciences Po Institutional REpository, LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.