![]() ![]() | SIMONETTO, T. J. A. (2024). Enhancing Machine Learning Robustness for Critical Industrial Systems: Constrained Adversarial Attacks and Distribution Drift Solutions [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/62239 |
![]() ![]() | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data. In Proceedings of The Thirty-Eighth Annual Conference on Neural Information Processing Systems. TBD. ![]() |
![]() ![]() | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases. In Proceedings of The Thirty-Eighth Annual Conference on Neural Information Processing Systems. TBD. ![]() |
![]() ![]() | SIMONETTO, T. J. A., GHAMIZI, S., & CORDY, M. (2024). Towards Adaptive Attacks on Constrained Tabular Machine Learning [Paper presentation]. ICML 2024 Workshop on the Next Generation of AI Safety. ![]() |
![]() ![]() | SIMONETTO, T. J. A., CORDY, M., GHAMIZI, S., LE TRAON, Y., Lefebvre, C., Boystov, A., & Goujon, A. (2024). On the Impact of Industrial Delays when Mitigating Distribution Drifts: an Empirical Study on Real-world Financial Systems. In KDD Workshop on Discovering Drift Phenomena in Evolving Data Landscape. Springer. doi:10.1007/978-3-031-82346-6_4 ![]() |
![]() ![]() | DYRMISHI, S., GHAMIZI, S., SIMONETTO, T. J. A., LE TRAON, Y., & CORDY, M. (2023). On the empirical effectiveness of unrealistic adversarial hardening against realistic adversarial attacks. In Conference Proceedings 2023 IEEE Symposium on Security and Privacy (SP) (pp. 1384-1400). IEEE. doi:10.1109/SP46215.2023.00049 ![]() |
![]() ![]() | SIMONETTO, T. J. A., DYRMISHI, S., GHAMIZI, S., CORDY, M., & LE TRAON, Y. (2022). A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 1313-1319). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2022/183 ![]() |