Kim, S., Yun, S., Lee, H., Gubri, M., Yoon, S., & Oh, S. J. (2023). ProPILE: Probing Privacy Leakage in Large Language Models. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Peer reviewed |
Gubri, M. (2023). What Matters in Model Training to Transfer Adversarial Examples [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/55429 |
Gubri, M., Cordy, M., & Le Traon, Y. (2023). Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/55436. |
Gubri, M., Cordy, M., Papadakis, M., Traon, Y. L., & Sen, K. (2022). LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. In Computer Vision -- ECCV 2022 (pp. 603--618). Springer Nature Switzerland. Peer reviewed |
Gubri, M., Cordy, M., Papadakis, M., Le Traon, Y., & Sen, K. (2022). Efficient and Transferable Adversarial Examples from Bayesian Neural Networks. The 38th Conference on Uncertainty in Artificial Intelligence. Peer reviewed |
FRANCI, A., CORDY, M., GUBRI, M., PAPADAKIS, M., & Traon, Y. (2022). Influence-driven data poisoning in graph-based semi-supervised classifiers. International Conference on AI Engineering: Software Engineering for AI, 77–87. doi:10.1145/3522664.3528606 Peer reviewed |
Ghamizi, S., Cordy, M., Gubri, M., Papadakis, M., Boystov, A., Le Traon, Y., & Goujon, A. (2020). Search-based adversarial testing and improvement of constrained credit scoring systems. In ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '20), November 8-13, 2020. Peer reviewed |