| Efficient search of first-order nash equilibria in nonconvex-concave smooth min-max problems DM Ostrovskii, A Lowy, M Razaviyayn SIAM Journal on Optimization 31 (4), 2508-2538, 2021 | 97 | 2021 |
| A Stochastic Optimization Framework for Fair Risk Minimization A Lowy, S Baharlouei, R Pavan, M Razaviyayn, A Beirami Transactions on Machine Learning Research, 2022 | 27* | 2022 |
| Private federated learning without a trusted server: Optimal algorithms for convex losses A Lowy, M Razaviyayn The Eleventh International Conference on Learning Representations (ICLR 2023), 2021 | 25* | 2021 |
| Private non-convex federated learning without a trusted server A Lowy, A Ghafelebashi, M Razaviyayn International Conference on Artificial Intelligence and Statistics (AISTATS …, 2022 | 16 | 2022 |
| Stochastic Differentially Private and Fair Learning A Lowy, D Gupta, M Razaviyayn The Eleventh International Conference on Learning Representations (ICLR 2023), 2022 | 9 | 2022 |
| Output perturbation for differentially private convex optimization with improved population loss bounds, runtimes and applications to private adversarial training A Lowy, M Razaviyayn arXiv preprint arXiv:2102.04704, 2021 | 9 | 2021 |
| Private stochastic optimization with large worst-case lipschitz parameter: Optimal rates for (non-smooth) convex losses and extension to non-convex losses A Lowy, M Razaviyayn International Conference on Algorithmic Learning Theory, 986-1054, 2023 | 8 | 2023 |
| Optimal differentially private model training with public data A Lowy, Z Li, T Huang, M Razaviyayn arXiv preprint arXiv:2306.15056, 2023 | 7* | 2023 |
| Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? A Lowy, Z Li, J Liu, T Koike-Akino, K Parsons, Y Wang arXiv preprint arXiv:2402.09540, 2024 | 1 | 2024 |
| Differentially Private and Fair Optimization for Machine Learning: Tight Error Bounds and Efficient Algorithms A Lowy University of Southern California, 2023 | 1 | 2023 |
| Efficient Differentially Private Fine-Tuning of Diffusion Models J Liu, A Lowy, T Koike-Akino, K Parsons, Y Wang arXiv e-prints, arXiv: 2406.05257, 2024 | | 2024 |
| How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization A Lowy, J Ullman, SJ Wright arXiv preprint arXiv:2402.11173, 2024 | | 2024 |
| Exploring User-level Gradient Inversion with a Diffusion Prior Z Li, A Lowy, J Liu, T Koike-Akino, BA Malin, K Parsons, Y Wang International Workshop on Federated Learning in the Age of Foundation Models …, 2023 | | 2023 |
| Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses C Gao, A Lowy, X Zhou, S Wright Forty-first International Conference on Machine Learning, 0 | | |
Meisam RazaviyaynUniversity of Southern California在 usc.edu 的电子邮件经过验证
