Guillaume Lecué
- Statistics and Probability top 2%
- Computational Mechanics top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty top 5%
- Co-authors
- Matthieu LerasleShahar MendelsonStéphane GaïffasKarine BertinHolger RauhutSjoerd DirksenPierre AlquierSimon Foucart
- Topics
- Statistical Methods and Inference (14 papers)Sparse and Compressive Sensing Techniques (10 papers)Advanced Statistical Methods and Models (6 papers)
In The Last Decade
Guillaume Lecué
27 papers receiving 264 citations
Peers
Comparison fields: 5 of 53
- Statistics and Probability 158
- Computational Mechanics 95
- Artificial Intelligence 87
- Computer Vision and Pattern Recognition 44
- Statistics, Probability and Uncertainty 36
Countries citing papers authored by Guillaume Lecué
This map shows the geographic impact of Guillaume Lecué'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 Guillaume Lecué with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guillaume Lecué more than expected).
Fields of papers citing papers by Guillaume Lecué
This network shows the impact of papers produced by Guillaume Lecué. 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 Guillaume Lecué. The network helps show where Guillaume Lecué may publish in the future.
Co-authorship network of co-authors of Guillaume Lecué
This figure shows the co-authorship network connecting the top 25 collaborators of Guillaume Lecué. A scholar is included among the top collaborators of Guillaume Lecué 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 Guillaume Lecué. Guillaume Lecué is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 5 | |
| 3 | 20 | |
| 4 | Median of means principle as a divide-and-conquer procedure for robustness, sub-sampling and hyper-parameters tuning | 1 |
| 5 | 12 | |
| 6 | 2 | |
| 7 | Robust machine learning by median-of-means : theory and practice | 49 |
| 8 | Learning from MOM's principles | 1 |
| 9 | Regularization and the small-ball method II: complexity dependent error rates | 7 |
| 10 | 9 | |
| 11 | 6 | |
| 12 | Compressed sensing under weak moment assumptions | 1 |
| 13 | 1 | |
| 14 | 4 | |
| 15 | 1 | |
| 16 | 4 | |
| 17 | 9 | |
| 18 | 5 | |
| 19 | 23 | |
| 20 | 9 |
About Guillaume Lecué
Guillaume Lecué is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Computational Mechanics, having authored 27 papers that have together received 279 indexed citations. Recurring topics across this work include Statistical Methods and Inference (14 papers), Sparse and Compressive Sensing Techniques (10 papers) and Advanced Statistical Methods and Models (6 papers). The work is most often cited by research in Statistics and Probability (158 citations), Computational Mathematics (4 citations) and Statistics, Probability and Uncertainty (36 citations). Guillaume Lecué has collaborated with scholars based in France, Israel and Australia. Frequent co-authors include Matthieu Lerasle, Shahar Mendelson, Stéphane Gaïffas, Karine Bertin, Holger Rauhut, Sjoerd Dirksen, Pierre Alquier, Simon Foucart, Bernard Host and Nathaël Gozlan. Their work appears in journals such as IEEE Transactions on Information Theory, The Annals of Statistics and Journal of Mathematical Analysis and Applications.
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.