Michèle Sébag

5.8k total citations
86 papers, 1.7k citations indexed

About

Michèle Sébag is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Michèle Sébag has authored 86 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 21 papers in Computational Theory and Mathematics and 17 papers in Computer Networks and Communications. Recurrent topics in Michèle Sébag's work include Metaheuristic Optimization Algorithms Research (20 papers), Evolutionary Algorithms and Applications (16 papers) and Machine Learning and Algorithms (12 papers). Michèle Sébag is often cited by papers focused on Metaheuristic Optimization Algorithms Research (20 papers), Evolutionary Algorithms and Applications (16 papers) and Machine Learning and Algorithms (12 papers). Michèle Sébag collaborates with scholars based in France, United Kingdom and China. Michèle Sébag's co-authors include Marc Schoenauer, Álvaro Fialho, Alexandre Termier, Ilya Loshchilov, Xiangliang Zhang, Cécile Germain‐Renaud, Luís Da Costa, Cyril Furtlehner, Olivier Teytaud and David López-Paz and has published in prestigious journals such as Bioinformatics, Communications of the ACM and Artificial Intelligence.

In The Last Decade

Michèle Sébag

82 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michèle Sébag France 24 1.0k 461 333 323 191 86 1.7k
Alexander L. Strehl United States 20 1.3k 1.3× 209 0.5× 262 0.8× 179 0.6× 176 0.9× 28 1.8k
Konstantinos G. Margaritis Greece 18 908 0.9× 306 0.7× 341 1.0× 202 0.6× 91 0.5× 117 1.5k
L. Darrell Whitley United States 27 1.9k 1.8× 867 1.9× 137 0.4× 559 1.7× 69 0.4× 71 3.1k
Mirosław Truszczyński United States 22 2.0k 1.9× 675 1.5× 112 0.3× 334 1.0× 145 0.8× 140 2.4k
William F. Clocksin United Kingdom 18 1.0k 1.0× 343 0.7× 213 0.6× 323 1.0× 98 0.5× 53 1.9k
R. K. Shyamasundar India 14 479 0.5× 334 0.7× 268 0.8× 740 2.3× 137 0.7× 132 1.8k
Philipp Rohlfshagen United Kingdom 13 1.3k 1.3× 206 0.4× 96 0.3× 194 0.6× 92 0.5× 24 2.1k
Nagarajan Natarajan United States 19 683 0.7× 182 0.4× 268 0.8× 306 0.9× 67 0.4× 63 1.6k
Prasad Tadepalli United States 23 1.4k 1.4× 205 0.4× 172 0.5× 149 0.5× 61 0.3× 108 1.7k
Kenneth Bacławski United States 19 637 0.6× 192 0.4× 280 0.8× 178 0.6× 95 0.5× 70 1.4k

Countries citing papers authored by Michèle Sébag

Since Specialization
Citations

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

Fields of papers citing papers by Michèle Sébag

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michèle Sébag

This figure shows the co-authorship network connecting the top 25 collaborators of Michèle Sébag. A scholar is included among the top collaborators of Michèle Sébag 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 Michèle Sébag. Michèle Sébag 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.
Sébag, Michèle, et al.. (2023). GAN-based data augmentation for transcriptomics: survey and comparative assessment. Bioinformatics. 39(Supplement_1). i111–i120. 17 indexed citations
2.
Mısır, Mustafa & Michèle Sébag. (2016). Alors: An algorithm recommender system. Artificial Intelligence. 244. 291–314. 38 indexed citations
3.
Guyon, Isabelle, Hugo Jair Escalante, Sérgio Escalera, et al.. (2016). A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. SPIRE - Sciences Po Institutional REpository. 28 indexed citations
4.
Schoenauer, Marc, et al.. (2014). Programming by Feedback. International Conference on Machine Learning. 1503–1511. 15 indexed citations
5.
Chevallier, Sylvain, Nicolas Bredèche, Hélène Paugam‐Moisy, & Michèle Sébag. (2011). Emergence of temporal and spatial synchronous behaviors in a foraging swarm. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
6.
Chevallier, Sylvain, Hélène Paugam‐Moisy, & Michèle Sébag. (2010). SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. HAL (Le Centre pour la Communication Scientifique Directe). 23. 379–387. 4 indexed citations
7.
Sébag, Michèle, et al.. (2010). Feature Selection as a One-Player Game. HAL (Le Centre pour la Communication Scientifique Directe). 359–366. 34 indexed citations
8.
Furtlehner, Cyril, Michèle Sébag, & Xiangliang Zhang. (2010). Scaling analysis of affinity propagation. Physical Review E. 81(6). 66102–66102. 5 indexed citations
9.
Zhang, Xiangliang, Cyril Furtlehner, & Michèle Sébag. (2008). Frugal and Online Affinity Propagation. SPIRE - Sciences Po Institutional REpository. 2 indexed citations
10.
Fialho, Álvaro, et al.. (2008). Adaptive operator selection with dynamic multi-armed bandits. HAL (Le Centre pour la Communication Scientifique Directe). 913–920. 79 indexed citations
11.
Baskiotis, Nicolas, et al.. (2007). A machine learning approach for statistical software testing. HAL (Le Centre pour la Communication Scientifique Directe). 2274–2279. 17 indexed citations
12.
Baskiotis, Nicolas, et al.. (2006). EXIST: Exploitation/Exploration Inference for Statistical Software Testing. HAL (Le Centre pour la Communication Scientifique Directe).
13.
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
14.
Gelly, Sylvain, et al.. (2004). Artificial Agents and Speculative Bubbles. WIT transactions on modelling and simulation. 38. 35–44. 1 indexed citations
15.
Botta, Marco, Attilio Giordana, Lorenza Saitta, & Michèle Sébag. (2003). Relational learning as search in a critical region. Journal of Machine Learning Research. 4. 431–463. 15 indexed citations
16.
Rouveirol, Céline, et al.. (2002). Constraint-based Learning of Long Relational Concepts. International Conference on Machine Learning. 35–42. 3 indexed citations
17.
Paechter, Ben, Thomas Bäck, Marc Schoenauer, et al.. (2002). A Distributed Resource Evolutionary Algorithm Machine (DREAM). Edinburgh Napier Research Repository (Edinburgh Napier University). 2. 951–958. 24 indexed citations
18.
Termier, Alexandre, Marie-Christine Rousset, & Michèle Sébag. (2001). Combining statistics and semantics for word and document clustering. 3. 47–52. 10 indexed citations
19.
Sébag, Michèle, et al.. (1997). Toward Civilized Evolution: Developing Inhibitions.. 291–298. 14 indexed citations
20.
Sébag, Michèle. (1996). Delaying the Choice of Bias: A Disjunctive Version Space Approach. HAL (Le Centre pour la Communication Scientifique Directe). 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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