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
- Bayesian Methods and Mixture Models 2
- Machine Learning and Data Classification 2
- Topic Modeling 2
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- Optimal Experimental Design Methods 4
- Co-authors
- Frank Wood (4 shared papers)Benjamin T. Vincent (1 shared paper)Hongseok Yang (2 shared papers)Robert Cornish (1 shared paper)Maximilian Igl (1 shared paper)Émile Mathieu (1 shared paper)N. Siddharth (2 shared papers)Sebastian M. Schmon (1 shared paper)
- Journals
- Journal of Machine Learning Research (1 paper)Statistical Science (1 paper)International Conference on Learning Representations (1 paper)Oxford University Research Archive (ORA) (University of Oxford) (1 paper)Open Science Framework (1 paper)
- Partner nations
- United KingdomSouth KoreaUnited States
In The Last Decade
Tom Rainforth
12 papers receiving 73 citations
Peers
Comparison fields: 5 of 48
- Statistics, Probability and Uncertainty 14
- Management Science and Operations Research 15
- General Decision Sciences 2
- Artificial Intelligence 31
- Computational Theory and Mathematics 15
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 25 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 | 36 | |
| 2 | Auto-Encoding Sequential Monte Carlo | 2018 | 10 |
| 3 | 2017 | 10 | |
| 4 | 2024 | 4 | |
| 5 | Disentangling Disentanglement | 2018 | 3 |
| 6 | 2017 | 3 | |
| 7 | 2016 | 3 | |
| 8 | 2020 | 3 | |
| 9 | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes | 2021 | 1 |
| 10 | Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support | 2020 | 1 |
| 11 | Improving Transformation Invariance in Contrastive Representation Learning | 2021 | 1 |
| 12 | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | 2020 | 1 |
| 13 | Variational Estimators for Bayesian Optimal Experimental Design. | 2019 | 0 |
| 14 | Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators | 2020 | 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 14 papers that have together received 76 indexed citations. Recurring topics across this work include Optimal Experimental Design Methods (4 papers), Gaussian Processes and Bayesian Inference (3 papers), Advanced Multi-Objective Optimization Algorithms (3 papers), Bayesian Methods and Mixture Models (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Machine Learning and Data Classification (2 papers), Statistical Methods and Inference (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (14 citations), Management Science and Operations Research (15 citations), General Decision Sciences (2 citations), Artificial Intelligence (31 citations) and Computational Theory and Mathematics (15 citations). Tom Rainforth has collaborated with scholars based in United Kingdom, South Korea and United States. Frequent co-authors include Frank Wood, Benjamin T. Vincent, Hongseok Yang, Robert Cornish, Maximilian Igl, Émile Mathieu, N. Siddharth, Sebastian M. Schmon, Andrew T. Campbell and Yee Whye Teh. Their work appears in journals such as Journal of Machine Learning Research, Statistical Science, International Conference on Learning Representations, Oxford University Research Archive (ORA) (University of Oxford) and Open Science Framework.
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.