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Andrew Lowy
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2020 – today
- 2024
- [c8]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. ICML 2024 - [c7]Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn:
Optimal Differentially Private Model Training with Public Data. ICML 2024 - [c6]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML 2024 - [i13]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 abs/2402.09540 (2024) - [i12]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR abs/2402.11173 (2024) - [i11]Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang:
Efficient Differentially Private Fine-Tuning of Diffusion Models. CoRR abs/2406.05257 (2024) - [i10]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 abs/2407.09690 (2024) - [i9]Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang:
Analyzing Inference Privacy Risks Through Gradients in Machine Learning. CoRR abs/2408.16913 (2024) - [i8]Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang:
Exploring User-level Gradient Inversion with a Diffusion Prior. CoRR abs/2409.07291 (2024) - 2023
- [c5]Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn:
Private Non-Convex Federated Learning Without a Trusted Server. AISTATS 2023: 5749-5786 - [c4]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: 986-1054 - [c3]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. ICLR 2023 - [c2]Andrew Lowy, Meisam Razaviyayn:
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. ICLR 2023 - [i7]Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn:
Optimal Differentially Private Learning with Public Data. CoRR abs/2306.15056 (2023) - 2022
- [j2]Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami:
A Stochastic Optimization Framework for Fair Risk Minimization. Trans. Mach. Learn. Res. 2022 (2022) - [c1]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. AFCP 2022: 86-119 - [i6]Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn:
Private Non-Convex Federated Learning Without a Trusted Server. CoRR abs/2203.06735 (2022) - [i5]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 abs/2209.07403 (2022) - [i4]Andrew Lowy, Devansh Gupta, Meisam Razaviyayn:
Stochastic Differentially Private and Fair Learning. CoRR abs/2210.08781 (2022) - 2021
- [j1]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): 2508-2538 (2021) - [i3]Andrew Lowy, Meisam Razaviyayn:
Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training. CoRR abs/2102.04704 (2021) - [i2]Andrew Lowy, Rakesh Pavan, Sina Baharlouei, Meisam Razaviyayn, Ahmad Beirami:
FERMI: Fair Empirical Risk Minimization via Exponential Rényi Mutual Information. CoRR abs/2102.12586 (2021) - [i1]Andrew Lowy, Meisam Razaviyayn:
Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds. CoRR abs/2106.09779 (2021)
Coauthor Index
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