Andrew Lowy - RankMe

31 papers A* 7A 1B 1Journal 20Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates. CoRR Andrew Lowy, Daogao Liu
2025	—	`conf`	Optimal Rates for Robust Stochastic Convex Optimization. FORC Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
2025	J	`jnl`	Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter. J. Priv. Confidentiality Andrew Lowy, Meisam Razaviyayn
2024	J	`jnl`	A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data. CoRR Devansh Gupta, A. S. Poornash, Andrew Lowy, Meisam Razaviyayn
2024	A*	`conf`	Analyzing Inference Privacy Risks Through Gradients In Machine Learning. CCS Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang
2024	J	`jnl`	Analyzing Inference Privacy Risks Through Gradients in Machine Learning. CoRR Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang
2024	J	`jnl`	Efficient Differentially Private Fine-Tuning of Diffusion Models. CoRR Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2024	J	`jnl`	Exploring User-level Gradient Inversion with a Diffusion Prior. CoRR Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang
2024	A*	`conf`	Faster Algorithms for User-Level Private Stochastic Convex Optimization. NeurIPS Andrew Lowy, Daogao Liu, Hilal Asi
2024	J	`jnl`	Faster Algorithms for User-Level Private Stochastic Convex Optimization. CoRR Andrew Lowy, Daogao Liu, Hilal Asi
2024	A*	`conf`	How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright
2024	J	`jnl`	How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright
2024	A*	`conf`	Optimal Differentially Private Model Training with Public Data. ICML Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn
2024	J	`jnl`	Optimal Rates for Robust Stochastic Convex Optimization. CoRR Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
2024	A*	`conf`	Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. ICML Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
2024	J	`jnl`	Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. CoRR Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
2024	J	`jnl`	Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? CoRR Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2023	J	`jnl`	Optimal Differentially Private Learning with Public Data. CoRR Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn
2023	A*	`conf`	Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. ICLR Andrew Lowy, Meisam Razaviyayn
2023	A	`conf`	Private Non-Convex Federated Learning Without a Trusted Server. AISTATS Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn
2023	B	`conf`	Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. ALT Andrew Lowy, Meisam Razaviyayn
2023	A*	`conf`	Stochastic Differentially Private and Fair Learning. ICLR Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
2022	J	`jnl`	A Stochastic Optimization Framework for Fair Risk Minimization. Trans. Mach. Learn. Res. Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami
2022	J	`jnl`	Private Non-Convex Federated Learning Without a Trusted Server. CoRR Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn
2022	J	`jnl`	Private Stochastic Optimization in the Presence of Outliers: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses. CoRR Andrew Lowy, Meisam Razaviyayn
2022	—	`conf`	Stochastic Differentially Private and Fair Learning. AFCP Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
2022	J	`jnl`	Stochastic Differentially Private and Fair Learning. CoRR Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
2021	J	`jnl`	Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems. SIAM J. Optim. Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn
2021	J	`jnl`	FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. CoRR Andrew Lowy, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami
2021	J	`jnl`	Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. CoRR Andrew Lowy, Meisam Razaviyayn
2021	J	`jnl`	Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training. CoRR Andrew Lowy, Meisam Razaviyayn