Andrew Lowy

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 18

Top Topics

Nash Equilibria

Diffusion Models

Risk Minimization

Convex Optimization

Top Venues

Publications

Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? CoRR (2024)
Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. CoRR (2024)
Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR (2024)
Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
Efficient Differentially Private Fine-Tuning of Diffusion Models. CoRR (2024)
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Stochastic Differentially Private and Fair Learning. ICLR (2023)
Andrew Lowy, Meisam Razaviyayn
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. ICLR (2023)
Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn
Optimal Differentially Private Learning with Public Data. CoRR (2023)
Andrew Lowy, Meisam Razaviyayn
Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. ALT (2023)
Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn
Private Non-Convex Federated Learning Without a Trusted Server. AISTATS (2023)
Andrew Lowy, Meisam Razaviyayn
Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. CoRR (2022)
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Stochastic Differentially Private and Fair Learning. AFCP (2022)
Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami
A Stochastic Optimization Framework for Fair Risk Minimization. Trans. Mach. Learn. Res. 2022 (2022)
Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn
Private Non-Convex Federated Learning Without a Trusted Server. CoRR (2022)
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Stochastic Differentially Private and Fair Learning. CoRR (2022)
Andrew Lowy, Meisam Razaviyayn
Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. CoRR (2021)
Andrew Lowy, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami
FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. CoRR (2021)
Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn
Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems. SIAM J. Optim. 31 (4) (2021)
Andrew Lowy, Meisam Razaviyayn
Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training. CoRR (2021)