Tom Rainforth

790 total citations
14 papers, 76 citations indexed

About

Tom Rainforth is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Tom Rainforth has authored 14 papers receiving a total of 76 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 4 papers in Management Science and Operations Research and 4 papers in Statistics and Probability. Recurrent topics in Tom Rainforth's work include Optimal Experimental Design Methods (4 papers), Advanced Multi-Objective Optimization Algorithms (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Tom Rainforth is often cited by papers focused on Optimal Experimental Design Methods (4 papers), Advanced Multi-Objective Optimization Algorithms (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Tom Rainforth collaborates with scholars based in United Kingdom, South Korea and Sweden. Tom Rainforth's co-authors include Frank Wood, Hongseok Yang, Benjamin T. Vincent, Maximilian Igl, Robert Cornish, Jason Yim, Sebastian M. Schmon, Regina Barzilay, Philip H. S. Torr and N. Siddharth and has published in prestigious journals such as Journal of Machine Learning Research, Statistical Science and Oxford University Research Archive (ORA) (University of Oxford).

In The Last Decade

Tom Rainforth

12 papers receiving 73 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tom Rainforth United Kingdom	4	31	15	15	14	12	14	76
Trieu H. Trinh United States	3	78 2.5×	17 1.1×	5 0.3×		16 1.3×	4	131
Steffen Finck Austria	5	63 2.0×	37 2.5×	11 0.7×	4 0.3×	1 0.1×	17	86
Jan Pfeifer United States	4	44 1.4×	8 0.5×	7 0.5×		11 0.9×	6	69
Jack Parker-Holder United Kingdom	4	60 1.9×	16 1.1×	7 0.5×		3 0.3×	11	78
Dominik Pająk Poland	6	22 0.7×	11 0.7×	5 0.3×		8 0.7×	18	90
Hemanta K. Maji United States	6	65 2.1×	25 1.7×	3 0.2×	2 0.1×	7 0.6×	19	92
Sonia Schulenburg United Kingdom	4	58 1.9×	14 0.9×	26 1.7×		4 0.3×	5	107
Jiri Hron United Kingdom	5	82 2.6×	3 0.2×	3 0.2×	1 0.1×	28 2.3×	8	104
Carlos Florensa United States	4	40 1.3×	7 0.5×	5 0.3×	1 0.1×	12 1.0×	5	68

Countries citing papers authored by Tom Rainforth

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Tom Rainforth

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Rainforth. A scholar is included among the top collaborators of Tom Rainforth 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 Tom Rainforth. Tom Rainforth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

Rainforth, Tom, et al.. (2024). Modern Bayesian Experimental Design. Statistical Science. 39(1). 36 indexed citations

Campbell, Andrew T., et al.. (2024). Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. PubMed. 4 indexed citations

Rudner, Tim G. J., et al.. (2021). On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. International Conference on Machine Learning. 9148–9156. 1 indexed citations

Rainforth, Tom, et al.. (2021). Improving Transformation Invariance in Contrastive Representation Learning. International Conference on Learning Representations. 1 indexed citations

Zhou, Yuan, Hongseok Yang, Yee Whye Teh, & Tom Rainforth. (2020). Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. arXiv (Cornell University). 1. 11534–11545. 1 indexed citations

Jankowiak, Martin, et al.. (2020). A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.. International Conference on Artificial Intelligence and Statistics. 2959–2969. 1 indexed citations

Rainforth, Tom, et al.. (2020). Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. Journal of Machine Learning Research. 21(88). 1–54.

Schmon, Sebastian M., et al.. (2020). Capturing Label Characteristics in VAEs. arXiv (Cornell University). 3 indexed citations

Jankowiak, Martin, et al.. (2019). Variational Estimators for Bayesian Optimal Experimental Design.. arXiv (Cornell University).

10.

Igl, Maximilian, et al.. (2018). Auto-Encoding Sequential Monte Carlo. Oxford University Research Archive (ORA) (University of Oxford). 10 indexed citations

11.

Mathieu, Émile, Tom Rainforth, N. Siddharth, & Yee Whye Teh. (2018). Disentangling Disentanglement. arXiv (Cornell University). 3 indexed citations

12.

Vincent, Benjamin T. & Tom Rainforth. (2017). The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design. Open Science Framework. 3 indexed citations

13.

Rainforth, Tom, et al.. (2017). On Nesting Monte Carlo Estimators. arXiv (Cornell University). 80. 4267–4276. 10 indexed citations

14.

Rainforth, Tom, Christian A. Naesseth, Fredrik Lindsten, et al.. (2016). Interacting Particle Markov Chain Monte Carlo. arXiv (Cornell University). 48. 2616–2625. 3 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.

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