Edward Snelson

2.3k total citations · 1 hit paper
10 papers, 1.3k citations indexed

About

Edward Snelson is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Edward Snelson has authored 10 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Control and Systems Engineering and 2 papers in Computational Theory and Mathematics. Recurrent topics in Edward Snelson's work include Gaussian Processes and Bayesian Inference (7 papers), Control Systems and Identification (5 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Edward Snelson is often cited by papers focused on Gaussian Processes and Bayesian Inference (7 papers), Control Systems and Identification (5 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Edward Snelson collaborates with scholars based in United Kingdom, Australia and Israel. Edward Snelson's co-authors include Zoubin Ghahramani, John Guiver, Carl Edward Rasmussen, Iain Murray, Oliver Williams, Laura Dietz and Ralf Herbrich and has published in prestigious journals such as Max Planck Institute for Plasma Physics, UCL Discovery (University College London) and Proceedings of the International AAAI Conference on Web and Social Media.

In The Last Decade

Edward Snelson

9 papers receiving 1.2k citations

Hit Papers

Sparse Gaussian Processes using Pseudo-inputs 2005 2026 2012 2019 2005 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edward Snelson United Kingdom 7 878 337 258 165 106 10 1.3k
Michalis K. Titsias United Kingdom 18 1.1k 1.3× 279 0.8× 193 0.7× 342 2.1× 100 0.9× 39 1.7k
Joaquin Quiñonero-Candela Germany 12 1.2k 1.4× 496 1.5× 274 1.1× 286 1.7× 212 2.0× 20 1.9k
Lehel Csató Romania 11 558 0.6× 259 0.8× 110 0.4× 139 0.8× 30 0.3× 34 836
David Luengo Spain 21 702 0.8× 133 0.4× 91 0.4× 168 1.0× 84 0.8× 85 1.4k
P.K. Simpson United States 10 1.4k 1.6× 281 0.8× 249 1.0× 422 2.6× 90 0.8× 31 1.9k
Lei Lei China 22 565 0.6× 165 0.5× 217 0.8× 208 1.3× 434 4.1× 89 1.3k
Dominic Grenier Canada 12 621 0.7× 120 0.4× 255 1.0× 112 0.7× 494 4.7× 54 1.3k
Chee-Yee Chong United States 24 1.1k 1.2× 746 2.2× 49 0.2× 111 0.7× 58 0.5× 92 2.0k
Yoshinobu Kawahara Japan 19 338 0.4× 170 0.5× 70 0.3× 171 1.0× 69 0.7× 85 1.1k
Carl G. Looney United States 14 522 0.6× 150 0.4× 294 1.1× 357 2.2× 87 0.8× 47 1.4k

Countries citing papers authored by Edward Snelson

Since Specialization
Citations

This map shows the geographic impact of Edward Snelson'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 Edward Snelson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Snelson more than expected).

Fields of papers citing papers by Edward Snelson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Edward Snelson. 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 Edward Snelson. The network helps show where Edward Snelson may publish in the future.

Co-authorship network of co-authors of Edward Snelson

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

All Works

10 of 10 papers shown
1.
Dietz, Laura, et al.. (2021). De-Layering Social Networks by Shared Tastes of Friendships. Proceedings of the International AAAI Conference on Web and Social Media. 6(1). 443–446.
2.
Guiver, John & Edward Snelson. (2009). Bayesian inference for Plackett-Luce ranking models. 377–384. 96 indexed citations
3.
Guiver, John & Edward Snelson. (2008). Learning to rank with SoftRank and Gaussian processes. 259–266. 33 indexed citations
4.
Snelson, Edward & Zoubin Ghahramani. (2007). Local and global sparse Gaussian process approximations.. Cambridge University Engineering Department Publications Database. 524–531. 147 indexed citations
5.
Snelson, Edward, et al.. (2007). Sensible Priors for Sparse Bayesian Learning. 13. 2 indexed citations
6.
Snelson, Edward & Zoubin Ghahramani. (2006). Variable noise and dimensionality reduction for sparse Gaussian processes. UCL Discovery (University College London). 461–468. 30 indexed citations
7.
Murray, Iain & Edward Snelson. (2006). Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment.: First PASCAL Machine Learning Challenges Workshop, Southampton, UK, April 11--13, 2005, Revised Selected Papers.. 3 indexed citations
8.
Snelson, Edward & Zoubin Ghahramani. (2005). Sparse Gaussian Processes using Pseudo-inputs. UCL Discovery (University College London). 18. 1257–1264. 787 indexed citations breakdown →
9.
Snelson, Edward & Zoubin Ghahramani. (2005). Compact approximations to Bayesian predictive distributions. 840–847. 14 indexed citations
10.
Snelson, Edward, Zoubin Ghahramani, & Carl Edward Rasmussen. (2003). Warped Gaussian Processes. Max Planck Institute for Plasma Physics. 16. 337–344. 145 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026