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)

FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. - Andrew Lowy; Rakesh Pavan; Sina Baharlouei; Meisam Razaviyayn; Ahmad Beirami (CoRR, 2021)

Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. - Andrew Lowy; Meisam Razaviyayn (CoRR, 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)

Stochastic Differentially Private and Fair Learning. - Andrew Lowy; Devansh Gupta; Meisam Razaviyayn (AFCP, 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)

A Stochastic Optimization Framework for Fair Risk Minimization. - Andrew Lowy; Sina Baharlouei; Rakesh Pavan; Meisam Razaviyayn; Ahmad Beirami (Trans. Mach. Learn. Res., 2022)

Private Non-Convex Federated Learning Without a Trusted Server. - Andrew Lowy; Ali Ghafelebashi; Meisam Razaviyayn (AISTATS, 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 (ALT, 2023)

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. - Andrew Lowy; Meisam Razaviyayn (ICLR, 2023)

Optimal Differentially Private Learning with Public Data. - Andrew Lowy; Zeman Li; Tianjian Huang; Meisam Razaviyayn (CoRR, 2023)

Analyzing Inference Privacy Risks Through Gradients In Machine Learning. - Zhuohang Li; Andrew Lowy; Jing Liu 009; Toshiaki Koike-Akino; Kieran Parsons; Bradley A. Malin; Ye Wang 001 (CCS, 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 001 (ICML, 2024)

Optimal Differentially Private Model Training with Public Data. - Andrew Lowy; Zeman Li; Tianjian Huang; Meisam Razaviyayn (ICML, 2024)

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. - Andrew Lowy; Jonathan R. Ullman; Stephen J. Wright 001 (ICML, 2024)

Faster Algorithms for User-Level Private Stochastic Convex Optimization. - Andrew Lowy; Daogao Liu; Hilal Asi (NeurIPS, 2024)

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? - Andrew Lowy; Zhuohang Li; Jing Liu 009; Toshiaki Koike-Akino; Kieran Parsons; Ye Wang 001 (CoRR, 2024)

Efficient Differentially Private Fine-Tuning of Diffusion Models. - Jing Liu 009; Andrew Lowy; Toshiaki Koike-Akino; Kieran Parsons; Ye Wang 001 (CoRR, 2024)

Exploring User-level Gradient Inversion with a Diffusion Prior. - Zhuohang Li; Andrew Lowy; Jing Liu 009; Toshiaki Koike-Akino; Bradley A. Malin; Kieran Parsons; Ye Wang 001 (CoRR, 2024)

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data. - Devansh Gupta; A. S. Poornash; Andrew Lowy; Meisam Razaviyayn (CoRR, 2024)

Optimal Rates for Robust Stochastic Convex Optimization. - Changyu Gao; Andrew Lowy; Xingyu Zhou; Stephen J. Wright 001 (FORC, 2025)

Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter. - Andrew Lowy; Meisam Razaviyayn (J. Priv. Confidentiality, 2025)

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates. - Andrew Lowy; Daogao Liu (CoRR, 2025)