| Margin-aware adversarial domain adaptation with optimal transport S Dhouib, I Redko, C Lartizien International conference on machine learning, 2514-2524, 2020 | 30 | 2020 |
| A swiss army knife for minimax optimal transport S Dhouib, I Redko, T Kerdoncuff, R Emonet, M Sebban International Conference on Machine Learning, 2504-2513, 2020 | 20 | 2020 |
| Revisiting -similarity learning for domain adaptation S Dhouib, I Redko Advances in Neural Information Processing Systems 31, 2018 | 8 | 2018 |
| Hypothesis transfer in bandits by weighted models S Bilaj, S Dhouib, S Maghsudi Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 3 | 2022 |
| Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization S Dhouib, S Maghsudi arXiv preprint arXiv:2203.05076, 2022 | 2 | 2022 |
| Meta Learning in Bandits within shared affine Subspaces S Bilaj, S Dhouib, S Maghsudi International Conference on Artificial Intelligence and Statistics, 523-531, 2024 | 1 | 2024 |
| On learning a large margin classifier for domain adaptation based on similarity functions S Dhouib, I Redko, C Lartizien 21 eme Conférence sur l'Apprentissage Automatique (CAp), 2019 | 1 | 2019 |
| Network Lasso Bandits S Dhouib, S Bilaj, B Nourani-Koliji, S Maghsudi | | 2024 |
| Contributions to unsupervised domain adaptation: Similarity functions, optimal transport and theoretical guarantees S Dhouib Université de Lyon, 2020 | | 2020 |
| Analyse théorique de l’apprentissage avec des fonctions de similarités pour l’adaptation de domaine S Dhouib, I Redko Conférence sur l'Apprentissage Automatique 2018, 2018 | | 2018 |
| Regret measure in continuous time limit for a stochastic Multi-armed bandit problem S Chebbi, S Dhouib, S Maghsudi | | |
| Clusters Agnostic Network Lasso Bandits S Dhouib, S Bilaj, B Nourani-Koliji, S Maghsudi | | |
Ievgen RedkoPrincipal research scientist, HDR, Paris Noah's Ark lab在 huawei.com 的電子郵件地址已通過驗證