Andrew Lowy
According to our database1,
Andrew Lowy
authored at least 13 papers
between 2021 and 2024.
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Bibliography
2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization.
CoRR, 2024
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
CoRR, 2024
2023
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses.
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.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
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.
CoRR, 2022
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.
SIAM J. Optim., 2021
Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds.
CoRR, 2021
CoRR, 2021
Output Perturbation for Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training.
CoRR, 2021