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Andrew Lowy
Andrew Lowy
Postdoctoral Research Associate, University of Wisconsin-Madison
在 usc.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
972021
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
162022
Stochastic Differentially Private and Fair Learning
A Lowy, D Gupta, M Razaviyayn
The Eleventh International Conference on Learning Representations (ICLR 2023), 2022
92022
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
92021
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
82023
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
12024
Differentially Private and Fair Optimization for Machine Learning: Tight Error Bounds and Efficient Algorithms
A Lowy
University of Southern California, 2023
12023
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
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