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Towards Securing Machine Learning Algorithms through Misclassification Detection and Adversarial Attack Detection. (Vers la Sécurisation des Algorithmes d'Apprentissage Automatique par Misclassification Detection et Adversarial Attack Detection).. Sapienza University of Rome, Italy, (2023)A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection., , , , , , and . Trans. Mach. Learn. Res., (2023)On the (Im)Possibility of Estimating Various Notions of Differential Privacy., , , , and . CoRR, (2022)Enhanced Models for Privacy and Utility in Continuous-Time Diffusion Networks., , and . ICTAC, volume 11884 of Lecture Notes in Computer Science, page 313-331. Springer, (2019)A Minimax Approach Against Multi-Armed Adversarial Attacks Detection., , , and . CoRR, (2023)Enhanced models for privacy and utility in continuous-time diffusion networks., , and . Int. J. Inf. Sec., 20 (5): 763-782 (2021)MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors., , , , and . ECML/PKDD (3), volume 13715 of Lecture Notes in Computer Science, page 286-303. Springer, (2022)DOCTOR: A Simple Method for Detecting Misclassification Errors., , , , and . NeurIPS, page 5669-5681. (2021)On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper)., , , , and . ICTCS, volume 3587 of CEUR Workshop Proceedings, page 219-224. CEUR-WS.org, (2023)Optimal Zero-Shot Detector for Multi-Armed Attacks., , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 2467-2475. PMLR, (2024)