Tom Rainforth
Impact in
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- Probabilistic and Robust Engineering Design
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- Optimal Experimental Design Methods
Papers in
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- Gaussian Processes and Bayesian Inference 3
- Domain Adaptation and Few-Shot Learning 2
- Topic Modeling 2
- Machine Learning and Data Classification 1
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- Optimal Experimental Design Methods 3
- Co-authors
- Adam Foster (3 shared papers)Maximilian Igl (1 shared paper)Frank Wood (2 shared papers)Zhikuan Zhao (1 shared paper)Benjamin Thomas Vincent (1 shared paper)Matthew J. O’Meara (1 shared paper)Tim G. J. Rudner (1 shared paper)Yee Whye Teh (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Statistical Science (1 paper)Durham Research Online (Durham University) (1 paper)International Conference on Machine Learning (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)
- Partner nations
- United KingdomUnited StatesPakistan
In The Last Decade
Tom Rainforth
10 papers receiving 78 citations
Peers
Comparison fields: 5 of 48
- Statistics, Probability and Uncertainty 14
- Management Science and Operations Research 17
- General Decision Sciences 2
- Computational Theory and Mathematics 17
- Industrial and Manufacturing Engineering 9
Countries citing papers authored by Tom Rainforth
This map shows the geographic impact of Tom Rainforth'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 Tom Rainforth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Rainforth more than expected).
Fields of papers citing papers by Tom Rainforth
This network shows the impact of papers produced by Tom Rainforth. 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 Tom Rainforth. The network helps show where Tom Rainforth may publish in the future.
Co-authors
The 13 scholars most cited alongside Tom Rainforth, 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 | 2024 | 51 | |
| 2 | 2020 | 10 | |
| 3 | Auto-Encoding Sequential Monte Carlo | 2018 | 9 |
| 4 | 2017 | 3 | |
| 5 | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | 2020 | 1 |
| 6 | Improving Transformation Invariance in Contrastive Representation Learning | 2021 | 1 |
| 7 | Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators | 2020 | 1 |
| 8 | 2021 | 1 | |
| 9 | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes | 2021 | 1 |
| 10 | 2025 | 1 | |
| 11 | On Statistical Bias In Active Learning: How and When to Fix It | 2021 | 0 |
About Tom Rainforth
Tom Rainforth is a scholar working on Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 79 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (3 papers), Optimal Experimental Design Methods (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Topic Modeling (2 papers), Multimodal Machine Learning Applications (2 papers), Machine Learning and Data Classification (1 paper) and Statistical Methods in Clinical Trials (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (14 citations), Management Science and Operations Research (17 citations), General Decision Sciences (2 citations), Computational Theory and Mathematics (17 citations) and Industrial and Manufacturing Engineering (9 citations). Tom Rainforth has collaborated with scholars based in United Kingdom, United States and Pakistan. Frequent co-authors include Adam Foster, Maximilian Igl, Frank Wood, Zhikuan Zhao, Benjamin Thomas Vincent, Matthew J. O’Meara, Tim G. J. Rudner, Yee Whye Teh, Yarin Gal and Philip H. S. Torr. Their work appears in journals such as Journal of Machine Learning Research, Statistical Science, Durham Research Online (Durham University), International Conference on Machine Learning and International Conference on Artificial Intelligence and Statistics.
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.