Onur Teymur

13.5k citations
7 papers · 11.3k indexed · 1 hit paper · h-index 3
Topics
Gaussian Processes and Bayesian Inference (3 papers)Neural Networks and Applications (2 papers)Bayesian Modeling and Causal Inference (2 papers)
Journals
Bayesian AnalysisarXiv (Cornell University)PubMed

In The Last Decade

Onur Teymur

7 papers receiving 11.1k citations

Hit Papers

Advances in Neural Information Processing Systems 29201620262019202220162.5k5.0k7.5k10.0k

Peers

Onur Teymur
Comparison fields: 5 of 221
  • Artificial Intelligence 4.7k
  • Computer Vision and Pattern Recognition 2.9k
  • Molecular Biology 875
  • Electrical and Electronic Engineering 680
  • Computational Theory and Mathematics 642
Replace Ben Calderhead with:
Ben Calderhead United Kingdom
Mark E. Shields United States
Krzysztof J. Cios United States
Dale Schuurmans Canada
Osmar R. Zaı̈ane Canada
P. M. Durai Raj Vincent India
Meelis Kull Estonia
Nal Kalchbrenner United Kingdom
David Warde-Farley Canada
Aja Huang United Kingdom
Onur Teymur relative to Ben Calderhead United Kingdom Ben Calderhead's profile →
Citations per field
00.5×1.5×
Ben Calderhead · 1×
Citations per year

Countries citing papers authored by Onur Teymur

Since Specialization
Citations

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

Fields of papers citing papers by Onur Teymur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Onur Teymur

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

All Works

7 of 7 papers shown
#WorkIndexed citations
1 1
2
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy
2
3 1
4
Implicit Probabilistic Integrators for ODEs
4
5
Advances in Neural Information Processing Systems 29breakdown →
11300
6 4
7 1

About Onur Teymur

Onur Teymur is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability and Modeling and Simulation, having authored 7 papers that have together received 11.3k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (3 papers), Neural Networks and Applications (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Artificial Intelligence (4.7k citations), Computer Vision and Pattern Recognition (2.9k citations) and Health Informatics (148 citations). Onur Teymur has collaborated with scholars based in United Kingdom, Australia and Germany. Frequent co-authors include Ben Calderhead, Jackson Gorham, Konstantinos C. Zygalakis, Tim Sullivan, Chris J. Oates, Sarah Filippi, François‐Xavier Briol and Philipp Hennig. Their work appears in journals such as Bayesian Analysis, arXiv (Cornell University) and PubMed.

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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