| CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning W Siblini, P Kuntz, F Meyer The 35th International Conference on Machine Learning (ICML 2018)., 4671-4680, 2018 | 65 | 2018 |
| A review on dimensionality reduction for multi-label classification W Siblini, P Kuntz, F Meyer IEEE Transactions on Knowledge and Data Engineering 33 (3), 839-857, 2019 | 60 | 2019 |
| Incremental learning strategies for credit cards fraud detection B Lebichot, GM Paldino, W Siblini, L He-Guelton, F Oblé, G Bontempi International Journal of Data Science and Analytics 12 (2), 165-174, 2021 | 36 | 2021 |
| Master your metrics with calibration W Siblini, J Fréry, L He-Guelton, F Oblé, YQ Wang International Symposium on Intelligent Data Analysis, 457-469, 2020 | 34 | 2020 |
| Multilingual question answering from formatted text applied to conversational agents W Siblini, C Pasqual, A Lavielle, M Challal, C Cauchois arXiv preprint arXiv:1910.04659, 2019 | 21 | 2019 |
| Reproducible machine learning for credit card fraud detection-practical handbook YA Le Borgne, W Siblini, B Lebichot, G Bontempi Université Libre de Bruxelles, 2022 | 19 | 2022 |
| NAG: neural feature aggregation framework for credit card fraud detection K Ghosh Dastidar, J Jurgovsky, W Siblini, M Granitzer Knowledge and Information Systems 64 (3), 831-858, 2022 | 16 | 2022 |
| Anomaly detection, consider your dataset first an illustration on fraud detection A Alazizi, A Habrard, F Jacquenet, L He-Guelton, F Oblé, W Siblini 2019 IEEE 31st international conference on tools with artificial …, 2019 | 13 | 2019 |
| Towards a more robust evaluation for conversational question answering W Siblini, B Sayil, Y Kessaci Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 10 | 2021 |
| Reproducible Machine Learning for Credit Card Fraud Detection-Practical Handbook. Université Libre de Bruxelles (2022) YA Le Borgne, W Siblini, B Lebichot, G Bontempi | 10 | |
| The importance of future information in credit card fraud detection KG Dastidar, M Granitzer, W Siblini International Conference on Artificial Intelligence and Statistics, 10067-10077, 2022 | 8 | 2022 |
| The role of diversity and ensemble learning in credit card fraud detection GM Paldino, B Lebichot, YA Le Borgne, W Siblini, F Oblé, G Boracchi, ... Advances in Data Analysis and Classification 18 (1), 193-217, 2024 | 5 | 2024 |
| Delaying interaction layers in transformer-based encoders for efficient open domain question answering W Siblini, M Challal, C Pasqual arXiv preprint arXiv:2010.08422, 2020 | 4 | 2020 |
| Transfer learning for credit card fraud detection: A journey from research to production W Siblini, G Coter, R Fabry, L He-Guelton, F Oblé, B Lebichot, ... arXiv preprint arXiv:2107.09323, 2021 | 3 | 2021 |
| Supervised feature space reduction for multi-label nearest neighbors W Siblini, R Alami, F Meyer, P Kuntz Advances in Artificial Intelligence: From Theory to Practice: 30th …, 2017 | 3 | 2017 |
| Multilingual Question Answering Applied to Conversational Agents W Siblini, C Pasqual, A Lavielle, M Challal, C Cauchois Advances in Knowledge Discovery and Management, 99-111, 2024 | 1 | 2024 |
| A count-sketch to reduce memory consumption when training a model with gradient descent W Siblini, F Meyer, P Kuntz 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 1 | 2019 |
| Apprentissage multi label extrême: comparaisons d'approches et nouvelles propositions W Siblini Nantes, 2018 | 1 | 2018 |
| Vipe: A new interactive classification framework for large sets of short texts-application to opinion mining W Siblini, F Meyer, P Kuntz arXiv preprint arXiv:1803.02101, 2018 | 1 | 2018 |
| Vipe: un outil interactif de classification multilabel de messages courts F Meyer, S Tricot, P Kuntz, W Siblini Extraction et Gestion des Connaissances. EGC 2017, 2017 | 1 | 2017 |
