Emilie Kaufmann
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Molecular Biology
- Management Science and Operations Research top 10%
- Materials Chemistry
- Co-authors
- Andrée Delahaye‐DuriezNathaniel KordaRémi MunosAurélien GarivierThomas BonaldMarc LelargeLilian BessonChristophe Moy
- Topics
- Advanced Bandit Algorithms Research (9 papers)Machine Learning and Algorithms (3 papers)Reinforcement Learning in Robotics (3 papers)
- Cited by
- Health InformaticsComputational Theory and MathematicsManagement Science and Operations Research
- Journals
- SHILAP Revista de lepidopterologíaThe Annals of StatisticsComputational and Structural Biotechnology Journal
- Partner nations
- FranceSwitzerlandCanada
In The Last Decade
Emilie Kaufmann
12 papers receiving 202 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 67
- Computational Theory and Mathematics 67
- Molecular Biology 62
- Management Science and Operations Research 47
- Materials Chemistry 26
Countries citing papers authored by Emilie Kaufmann
This map shows the geographic impact of Emilie Kaufmann'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 Emilie Kaufmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emilie Kaufmann more than expected).
Fields of papers citing papers by Emilie Kaufmann
This network shows the impact of papers produced by Emilie Kaufmann. 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 Emilie Kaufmann. The network helps show where Emilie Kaufmann may publish in the future.
Co-authorship network of co-authors of Emilie Kaufmann
This figure shows the co-authorship network connecting the top 25 collaborators of Emilie Kaufmann. A scholar is included among the top collaborators of Emilie Kaufmann 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 Emilie Kaufmann. Emilie Kaufmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | On Multi-Armed Bandit Designs for Phase I Clinical Trials. | 1 |
| 4 | New Algorithms for Multiplayer Bandits when Arm Means Vary Among Players | 1 |
| 5 | 5 | |
| 6 | 141 | |
| 7 | 5 | |
| 8 | 2 | |
| 9 | Multi-Player Bandits Models Revisited | 1 |
| 10 | 5 | |
| 11 | 6 | |
| 12 | 28 | |
| 13 | Thompson Sampling: An Optimal Finite Time Analysis | 10 |
About Emilie Kaufmann
Emilie Kaufmann is a scholar working on Management Science and Operations Research, Software and Computer Science Applications, having authored 13 papers that have together received 211 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (9 papers), Machine Learning and Algorithms (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Health Informatics (9 citations), Computational Theory and Mathematics (67 citations) and Management Science and Operations Research (47 citations). Emilie Kaufmann has collaborated with scholars based in France, Switzerland and Canada. Frequent co-authors include Andrée Delahaye‐Duriez, Nathaniel Korda, Rémi Munos, Aurélien Garivier, Rémi Munos, Thomas Bonald, Marc Lelarge, Lilian Besson, Christophe Moy and Lars Englberger. Their work appears in journals such as SHILAP Revista de lepidopterología, The Annals of Statistics and Computational and Structural Biotechnology Journal.
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.