Olivier Teytaud

3.6k total citations
101 papers, 1.2k citations indexed

About

Olivier Teytaud is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Olivier Teytaud has authored 101 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Artificial Intelligence, 22 papers in Computational Theory and Mathematics and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Olivier Teytaud's work include Metaheuristic Optimization Algorithms Research (32 papers), Artificial Intelligence in Games (30 papers) and Evolutionary Algorithms and Applications (27 papers). Olivier Teytaud is often cited by papers focused on Metaheuristic Optimization Algorithms Research (32 papers), Artificial Intelligence in Games (30 papers) and Evolutionary Algorithms and Applications (27 papers). Olivier Teytaud collaborates with scholars based in France, Taiwan and China. Olivier Teytaud's co-authors include Sylvain Gelly, Yizao Wang, Rémi Munos, Chang-Shing Lee, A. Moreau, Mei‐Hui Wang, Anne Auger, Michèle Sébag, Arpad Rimmel and Marc Schoenauer and has published in prestigious journals such as PLoS ONE, Communications of the ACM and IEEE Transactions on Image Processing.

In The Last Decade

Olivier Teytaud

96 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Olivier Teytaud France 19 843 205 183 164 123 101 1.2k
Sylvain Gelly France 17 1.2k 1.5× 67 0.3× 311 1.7× 235 1.4× 147 1.2× 46 1.7k
Wojciech Marian Czarnecki Poland 15 827 1.0× 165 0.8× 40 0.2× 78 0.5× 99 0.8× 36 1.5k
Jonathan Baxter Australia 15 782 0.9× 73 0.4× 72 0.4× 43 0.3× 105 0.9× 26 1.4k
Lashon B. Booker United States 10 1000 1.2× 356 1.7× 24 0.1× 48 0.3× 84 0.7× 22 1.7k
Kyung-Joong Kim South Korea 21 556 0.7× 66 0.3× 38 0.2× 206 1.3× 36 0.3× 151 1.4k
Peifa Jia China 15 453 0.5× 39 0.2× 36 0.2× 54 0.3× 90 0.7× 71 847
Shotaro Akaho Japan 15 510 0.6× 58 0.3× 28 0.2× 41 0.3× 98 0.8× 100 981
Ulf Brefeld Germany 20 891 1.1× 50 0.2× 130 0.7× 26 0.2× 63 0.5× 64 1.4k
Jin Tian United States 18 504 0.6× 44 0.2× 62 0.3× 25 0.2× 112 0.9× 98 1.0k
Wenbin Zhang United States 19 607 0.7× 27 0.1× 41 0.2× 65 0.4× 85 0.7× 139 1.5k

Countries citing papers authored by Olivier Teytaud

Since Specialization
Citations

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

Fields of papers citing papers by Olivier Teytaud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Olivier Teytaud

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

All Works

20 of 20 papers shown
1.
Najman, Laurent, et al.. (2025). Evolutionary Retrofitting. arXiv (Cornell University). 6(1). 1–45. 1 indexed citations
2.
Schoenauer, Marc, et al.. (2023). Interactive Latent Diffusion Model. Proceedings of the Genetic and Evolutionary Computation Conference. 586–596. 1 indexed citations
3.
Raponi, Elena, et al.. (2023). Optimizing With Low Budgets: A Comparison on the Black-Box Optimization Benchmarking Suite and OpenAI Gym. IEEE Transactions on Evolutionary Computation. 29(1). 91–101. 3 indexed citations
4.
Meunier, Laurent, Baptiste Rozière, Jérémy Rapin, et al.. (2020). Black-Box Optimization Revisited: Improving Algorithm Selection Wizards\n through Massive Benchmarking. arXiv (Cornell University). 21 indexed citations
5.
Barry, Mamadou Aliou, et al.. (2020). Evolutionary algorithms converge towards evolved biological photonic structures. CUNY Academic Works (City University of New York). 22 indexed citations
6.
Liu, Jialin, et al.. (2015). Simple and cumulative regret for continuous noisy optimization. Theoretical Computer Science. 617. 12–27. 6 indexed citations
7.
Teytaud, Olivier, et al.. (2012). THE FRONTIER OF DECIDABILITY IN PARTIALLY OBSERVABLE RECURSIVE GAMES. International Journal of Foundations of Computer Science. 23(7). 1439–1450. 1 indexed citations
8.
Gelly, Sylvain, Levente Kocsis, Marc Schoenauer, et al.. (2012). The grand challenge of computer Go. Communications of the ACM. 55(3). 106–113. 82 indexed citations
9.
Teytaud, Olivier, et al.. (2010). Log(lambda) Modifications for Optimal Parallelism.. HAL (Le Centre pour la Communication Scientifique Directe). 254–263. 1 indexed citations
10.
Lee, Chang-Shing, Martin Müller, & Olivier Teytaud. (2010). Special Issue on Monte Carlo Techniques and Computer Go. IEEE Transactions on Computational Intelligence and AI in Games. 2(4). 225–228. 18 indexed citations
11.
Hoock, Jean‐Baptiste, et al.. (2009). 9x9 GO AS BLACK WITH KOMI 7.5: AT LAST SOME GAMES WON AGAINST TOP PLAYERS IN THE DISADVANTAGEOUS SITUATION. ICGA Journal. 3 indexed citations
12.
Chaslot, Guillaume, et al.. (2009). On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers. HAL (Le Centre pour la Communication Scientifique Directe). 3 indexed citations
13.
Sébag, Michèle, et al.. (2009). Optimal robust expensive optimization is tractable. 1951–1956. 3 indexed citations
14.
Gelly, Sylvain, et al.. (2008). On the Parallelization of Monte-Carlo planning. SPIRE - Sciences Po Institutional REpository. 20 indexed citations
15.
Teytaud, Olivier. (2007). Conditionning, halting criteria and choosing lambda. Scientific Programming. 1 indexed citations
16.
Gelly, Sylvain, Jérémie Mary, & Olivier Teytaud. (2006). Learning for stochastic dynamic programming. SPIRE - Sciences Po Institutional REpository. 191–196. 8 indexed citations
17.
Gelly, Sylvain, et al.. (2006). Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in EDA, and implementations with billiards. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
18.
Sébag, Michèle, et al.. (2005). Multi-objective Multi-modal Optimization for Mining Spatio-temporal Patterns.. 61(12). 217–230. 1 indexed citations
19.
Flin, Frédéric, Jean-Bruno Brzoska, David Cœurjolly, et al.. (2005). Adaptive estimation of normals and surface area for discrete 3-D objects: application to snow binary data from X-ray tomography. IEEE Transactions on Image Processing. 14(5). 585–596. 47 indexed citations
20.
Sébag, Michèle, et al.. (2005). A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns. International Joint Conference on Artificial Intelligence. 859–864. 18 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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