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Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family., , , , , , and . CoRR, (2019)On the robustness of randomized classifiers to adversarial examples., , , , , and . Mach. Learn., 111 (9): 3425-3457 (2022)Combining Differential Privacy and Byzantine Resilience in Distributed SGD., , , , and . CoRR, (2021)Practical Homomorphic Aggregation for Byzantine ML., , , , , and . CoRR, (2023)On the Impossible Safety of Large AI Models., , , , , , and . CoRR, (2022)Byzantine Machine Learning: A Primer., , and . ACM Comput. Surv., 56 (7): 169:1-169:39 (July 2024)SPEED: secure, PrivatE, and efficient deep learning., , , , and . Mach. Learn., 110 (4): 675-694 (2021)Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis., , , , and . CoRR, (2022)Distributed Learning with Curious and Adversarial Machines., , , , and . CoRR, (2023)Randomization matters How to defend against strong adversarial attacks., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7717-7727. PMLR, (2020)