| Coupling algebraic topology theory, formal methods and safety requirements toward a new coverage metric for artificial intelligence models F Adjed, M Mziou-Sallami, F Pelliccia, M Rezzoug, L Schott, C Bohn, ... Neural Computing and Applications 34 (19), 17129-17144, 2022 | 8 | 2022 |
| Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network L Schott, H Hajri, S Lamprier 2022 International Joint Conference on Neural Networks (IJCNN), 2022 | 8 | 2022 |
| Stochastic sparse adversarial attacks M Césaire, L Schott, H Hajri, S Lamprier, P Gallinari 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 7 | 2021 |
| Neural adversarial attacks with random noises H Hajri, M Cesaire, L Schott, S Lamprier, P Gallinari International Journal on Artificial Intelligence Tools, 2022 | 3 | 2022 |
| Certification of deep reinforcement learning with multiple outputs using abstract interpretation and safety critical systems F Adjed, F Pelliccia, M Rezzoug, L Schott Proceedings of the 31st European safety and reliability conference, 3185-3191, 2021 | 1 | 2021 |
| Plateforme d'assistants interactifs pour les opérations en temps réel M Meddeb, K Amokrane, R Kaddah, L Schott, D Wright, AA Said HyCHA'24-Journées d'Intelligence Artificielle Hybride: de l'intégration des …, 2024 | | 2024 |
| Robust Deep Reinforcement Learning Through Adversarial Attacks and Training: A Survey L Schott, J Delas, H Hajri, E Gherbi, R Yaich, N Boulahia-Cuppens, ... arXiv preprint arXiv:2403.00420, 2024 | | 2024 |
| On Improving the Robustness of Reinforcement Learning Policies against Adversarial Attacks Y Jaafra, C Bohn, L Schott, F Adjed, F Pelliccia, M Rezzoug ESREL 2022, 2022 | | 2022 |
