| The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure S Mahloujifar, DI Diochnos, M Mahmoody Proceedings of the AAAI Conference on Artificial Intelligence 33, 4536-4543, 2019 | 144 | 2019 |
| Improving adversarial robustness using proxy distributions V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal arXiv preprint arXiv:2104.09425 1, 2021 | 99* | 2021 |
| Adversarial risk and robustness: General definitions and implications for the uniform distribution D Diochnos, S Mahloujifar, M Mahmoody Advances in Neural Information Processing Systems 31, 2018 | 72 | 2018 |
| Universal Multi-Party Poisoning Attacks S Mahloujifar, M Mahmoody, A Mohammed International Conference on Machine Learning, 4274-4283, 2019 | 67* | 2019 |
| Is private learning possible with instance encoding? N Carlini, S Deng, S Garg, S Jha, S Mahloujifar, M Mahmoody, A Thakurta, ... 2021 IEEE Symposium on Security and Privacy (SP), 410-427, 2021 | 65* | 2021 |
| Property inference from poisoning S Mahloujifar, E Ghosh, M Chase 2022 IEEE Symposium on Security and Privacy (SP), 1120-1137, 2022 | 48* | 2022 |
| Can Adversarially Robust Learning Leverage Computational Hardness? S Mahloujifar, M Mahmoody [edit] Saeed Mahloujifar, Mohammad Mahmoody ; Proceedings of the 30th …, 2019 | 42 | 2019 |
| Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification A Panda, S Mahloujifar, AN Bhagoji, S Chakraborty, P Mittal International Conference on Artificial Intelligence and Statistics, 7587-7624, 2022 | 39 | 2022 |
| Mitigating membership inference attacks by {Self-Distillation} through a novel ensemble architecture X Tang, S Mahloujifar, L Song, V Shejwalkar, M Nasr, A Houmansadr, ... 31st USENIX Security Symposium (USENIX Security 22), 1433-1450, 2022 | 33 | 2022 |
| Model-targeted poisoning attacks with provable convergence F Suya, S Mahloujifar, A Suri, D Evans, Y Tian International Conference on Machine Learning, 10000-10010, 2021 | 32* | 2021 |
| Learning under -Tampering Attacks S Mahloujifar, DI Diochnos, M Mahmoody Proceedings of Algorithmic Learning Theory, PMLR 83:572-596, 2018., 2018 | 30 | 2018 |
| {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier C Xiang, S Mahloujifar, P Mittal 31st USENIX Security Symposium (USENIX Security 22), 2065-2082, 2022 | 28 | 2022 |
| Blockwise p-Tampering Attacks on Cryptographic Primitives, Extractors, and Learners S Mahloujifar, M Mahmoody Theory of Cryptography: 15th International Conference, TCC 2017, Baltimore …, 2017 | 25 | 2017 |
| Parameterizing activation functions for adversarial robustness S Dai, S Mahloujifar, P Mittal 2022 IEEE Security and Privacy Workshops (SPW), 80-87, 2022 | 22 | 2022 |
| Empirically measuring concentration: Fundamental limits on intrinsic robustness S Mahloujifar, X Zhang, M Mahmoody, D Evans Advances in Neural Information Processing Systems 32, 2019 | 22 | 2019 |
| Revisiting the assumption of latent separability for backdoor defenses X Qi, T Xie, Y Li, S Mahloujifar, P Mittal The eleventh international conference on learning representations, 2022 | 21 | 2022 |
| Adversarially robust learning could leverage computational hardness S Garg, S Jha, S Mahloujifar, M Mahmoody Proceedings of the 31st International Conference on Algorithmic Learning …, 2020 | 21 | 2020 |
| Lower bounds for adversarially robust pac learning DI Diochnos, S Mahloujifar, M Mahmoody arXiv preprint arXiv:1906.05815, 2019 | 20 | 2019 |
| Computational concentration of measure: Optimal bounds, reductions, and more O Etesami, S Mahloujifar, M Mahmoody Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020 | 15 | 2020 |
| Membership inference on word embedding and beyond S Mahloujifar, HA Inan, M Chase, E Ghosh, M Hasegawa arXiv preprint arXiv:2106.11384, 2021 | 13 | 2021 |
Mohammad MahmoodyAssociate Professor, University of VirginiaVerified email at virginia.edu
