Profil

SIMONETTO Thibault Jean Angel

University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal

Main Referenced Co-authors
CORDY, Maxime  (6)
GHAMIZI, Salah (3)
GHAMIZI, Salah  (3)
LE TRAON, Yves  (3)
DYRMISHI, Salijona  (2)
Main Referenced Keywords
adversarial attacks (3); constrained machine learning (3); machine learning (3); security (3); tabular data (3);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SerVal - Security, Reasoning & Validation (4)
NCER-FT - FinTech National Centre of Excellence in Research (4)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) (1)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Other (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (7)

Publications (total 7)

The most downloaded
163 downloads
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 https://hdl.handle.net/10993/62239

The most cited

8 citations (OpenAlex)

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 https://hdl.handle.net/10993/53045

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.
Peer reviewed

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.
Peer reviewed

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.
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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