Andrew Lowy

According to our database, Andrew Lowy authored at least 19 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Faster Algorithms for User-Level Private Stochastic Convex Optimization.

Andrew Lowy, Daogao Liu, Hilal Asi

CoRR, 2024

Exploring User-level Gradient Inversion with a Diffusion Prior.

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang

CoRR, 2024

Efficient Differentially Private Fine-Tuning of Diffusion Models.

Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, 2024

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, 2024

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization.

Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Differentially Private Model Training with Public Data.

Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses.

Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Analyzing Inference Privacy Risks Through Gradients In Machine Learning.

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

2023

Optimal Differentially Private Learning with Public Data.

Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn

CoRR, 2023

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses.

Andrew Lowy, Meisam Razaviyayn

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses.

Andrew Lowy, Meisam Razaviyayn

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

Private Non-Convex Federated Learning Without a Trusted Server.

Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Stochastic Optimization Framework for Fair Risk Minimization.

Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami

Trans. Mach. Learn. Res., 2022

Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses.

Andrew Lowy, Meisam Razaviyayn

CoRR, 2022

Stochastic Differentially Private and Fair Learning.

Andrew Lowy, Devansh Gupta, Meisam Razaviyayn

Proceedings of the Algorithmic Fairness through the Lens of Causality and Privacy Workshop, 2022

2021

Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems.

Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn

SIAM J. Optim., 2021

Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds.

Andrew Lowy, Meisam Razaviyayn

CoRR, 2021

FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information.

Andrew Lowy, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami

CoRR, 2021

Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training.

Andrew Lowy, Meisam Razaviyayn

CoRR, 2021