Tanguy Urvoy
- Artificial Intelligence top 10%
- Information Systems top 10%
- Management Science and Operations Research top 10%
- Computer Networks and Communications
- Sociology and Political Science
- Co-authors
- Thomas LavergneFabrice ClérotFrançois YvonRaphaël FéraudLina M. Rojas BarahonaArtem SokolovStefan RiezlerFabrice Lefèvre
- Topics
- Advanced Bandit Algorithms Research (6 papers)Topic Modeling (5 papers)Spam and Phishing Detection (4 papers)
In The Last Decade
Tanguy Urvoy
14 papers receiving 145 citations
Peers
Comparison fields: 5 of 21
- Artificial Intelligence 106
- Information Systems 86
- Management Science and Operations Research 40
- Computer Networks and Communications 24
- Sociology and Political Science 22
Countries citing papers authored by Tanguy Urvoy
This map shows the geographic impact of Tanguy Urvoy'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 Tanguy Urvoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tanguy Urvoy more than expected).
Fields of papers citing papers by Tanguy Urvoy
This network shows the impact of papers produced by Tanguy Urvoy. 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 Tanguy Urvoy. The network helps show where Tanguy Urvoy may publish in the future.
Co-authorship network of co-authors of Tanguy Urvoy
This figure shows the co-authorship network connecting the top 25 collaborators of Tanguy Urvoy. A scholar is included among the top collaborators of Tanguy Urvoy 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 Tanguy Urvoy. Tanguy Urvoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | Scaling up budgeted reinforcement learning. | 2 |
| 5 | Random Forest for the Contextual Bandit Problem | 13 |
| 6 | Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation | 4 |
| 7 | Generic Exploration and K-armed Voting Bandits | 21 |
| 8 | Generic Exploration and K-armed Voting Bandits (extended version) | 1 |
| 9 | 2 | |
| 10 | A stochastic bandit algorithm for scratch games | 3 |
| 11 | 2 | |
| 12 | Detecting fake content with relative entropy scoring | 15 |
| 13 | 62 | |
| 14 | Tracking Web Spam with Hidden Style Similarity | 29 |
About Tanguy Urvoy
Tanguy Urvoy is a scholar working on Management Science and Operations Research, Artificial Intelligence and Information Systems, having authored 14 papers that have together received 157 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (6 papers), Topic Modeling (5 papers) and Spam and Phishing Detection (4 papers). The work is most often cited by research in Information Systems (86 citations), Artificial Intelligence (106 citations) and Management Science and Operations Research (40 citations). Tanguy Urvoy has collaborated with scholars based in France and Germany. Frequent co-authors include Thomas Lavergne, Fabrice Clérot, François Yvon, Raphaël Féraud, Lina M. Rojas Barahona, Artem Sokolov, Stefan Riezler, Fabrice Lefèvre, Edouard Leurent and Romain Laroche. Their work appears in journals such as Machine Learning, Language Resources and Evaluation and ACM Transactions on the Web.
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.