Andrew Lowy

According to our database, Andrew Lowy authored at least 24 papers between 2021 and 2026.

Collaborative distances:

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

2026

Optimal Rates for Pure {\varepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails.

Andrew Lowy

CoRR, April, 2026

2025

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates.

Andrew Lowy, Daogao Liu

CoRR, June, 2025

Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter.

Andrew Lowy, Meisam Razaviyayn

J. Priv. Confidentiality, 2025

Optimal Rates for Robust Stochastic Convex Optimization.

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

Proceedings of the 6th Symposium on Foundations of Responsible Computing, 2025

2024

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data.

Devansh Gupta, A. S. Poornash, Andrew Lowy, Meisam Razaviyayn

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

Faster Algorithms for User-Level Private Stochastic Convex Optimization.

Andrew Lowy, Daogao Liu, Hilal Asi

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Stochastic Differentially Private and Fair Learning.

Andrew Lowy, Devansh Gupta, 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

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