Aurélien Bibaut
Impact in
-
- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- AI in cancer detection
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
Papers in
-
- Advanced Causal Inference Techniques 4
- Statistical Methods and Inference 3
- Statistical Methods in Clinical Trials 2
- Co-authors
- Mark van der Laan (1 shared paper)Cheng Ju (1 shared paper)Mark J. van der Laan (3 shared papers)Nikos Vlassis (2 shared papers)Tony Jebara (1 shared paper)Benjamin F. Arnold (1 shared paper)Jean Frantz Lemoine (1 shared paper)Leda Hernandez (1 shared paper)
- Journals
- Scientific Reports (1 paper)Annual Review of Statistics and Its Application (1 paper)Journal of Applied Statistics (1 paper)Computational Statistics (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesPhilippinesMalawi
In The Last Decade
Aurélien Bibaut
8 papers receiving 304 citations
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 76
- Artificial Intelligence 121
- Radiology, Nuclear Medicine and Imaging 58
- Media Technology 21
- Health Informatics 2
Countries citing papers authored by Aurélien Bibaut
This map shows the geographic impact of Aurélien Bibaut'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 Aurélien Bibaut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurélien Bibaut more than expected).
Fields of papers citing papers by Aurélien Bibaut
This network shows the impact of papers produced by Aurélien Bibaut. 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 Aurélien Bibaut. The network helps show where Aurélien Bibaut may publish in the future.
Co-authors
The 17 scholars most cited alongside Aurélien Bibaut, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 274 | |
| 2 | 2020 | 20 | |
| 3 | On the Design of Estimators for Bandit Off-Policy Evaluation | 2019 | 7 |
| 4 | 2019 | 4 | |
| 5 | 2018 | 3 | |
| 6 | 2024 | 1 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 1 |
About Aurélien Bibaut
Aurélien Bibaut is a scholar working on Statistics and Probability, Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 311 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (4 papers), Statistical Methods and Inference (3 papers), Statistical Methods in Clinical Trials (2 papers), Advanced Bandit Algorithms Research (2 papers), Data-Driven Disease Surveillance (1 paper), Zoonotic diseases and public health (1 paper), Image and Signal Denoising Methods (1 paper) and Economic and Environmental Valuation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (76 citations), Artificial Intelligence (121 citations), Radiology, Nuclear Medicine and Imaging (58 citations), Media Technology (21 citations) and Health Informatics (2 citations). Aurélien Bibaut has collaborated with scholars based in United States, Philippines and Malawi. Frequent co-authors include Mark van der Laan, Cheng Ju, Mark J. van der Laan, Nikos Vlassis, Tony Jebara, Benjamin F. Arnold, Jean Frantz Lemoine, Leda Hernandez, Aboulaye Meïté and Lazarus Juziwelo. Their work appears in journals such as Scientific Reports, Annual Review of Statistics and Its Application, Journal of Applied Statistics, Computational Statistics and International Conference on Machine Learning.
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.