2 citations · 8 across the 12 of their papers we have counts for
14 papers
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Andrew Lowy, Daogao Liu
Bilevel optimization, in which one optimization problem is nested inside another, underlies many machine learning applications with a hierarchical structure -- such as meta-learnin…
Optimal Rates for Robust Stochastic Convex Optimization
Changyu Gao, Andrew Lowy, Xingyu Zhou +1
Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniq…
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
Devansh Gupta, A. S. Poornash, Andrew Lowy +1
Machine learning models are often trained on sensitive data (e.g., medical records and race/gender) that is distributed across different "silos" (e.g., hospitals). These federated…
Faster Algorithms for User-Level Private Stochastic Convex Optimization
Andrew Lowy, Daogao Liu, Hilal Asi
We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are users (e.g., cell phones), each possess…
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Changyu Gao, Andrew Lowy, Xingyu Zhou +1
We revisit the problem of federated learning (FL) with private data from people who do not trust the server or other silos/clients. In this context, every silo (e.g. hospital) has…
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
Andrew Lowy, Jonathan Ullman, Stephen J. Wright
We provide a simple and flexible framework for designing differentially private algorithms to find approximate stationary points of non-convex loss functions. Our framework is base…
Optimal Differentially Private Model Training with Public Data
Andrew Lowy, Zeman Li, Tianjian Huang +1
Differential privacy (DP) ensures that training a machine learning model does not leak private data. In practice, we may have access to auxiliary public data that is free of privac…
Stochastic Differentially Private and Fair Learning
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against cert…
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
Andrew Lowy, Meisam Razaviyayn
We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data may be extremely large or infinite. To date…
Private Non-Convex Federated Learning Without a Trusted Server
Andrew Lowy, Ali Ghafelebashi, Meisam Razaviyayn
We study federated learning (FL) -- especially cross-silo FL -- with non-convex loss functions and data from people who do not trust the server or other silos. In this setting, eac…
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
Andrew Lowy, Meisam Razaviyayn
This paper studies federated learning (FL)--especially cross-silo FL--with data from people who do not trust the server or other silos. In this setting, each silo (e.g. hospital) h…
A Stochastic Optimization Framework for Fair Risk Minimization
Andrew Lowy, Sina Baharlouei, Rakesh Pavan +2
Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibil…
Output Perturbation for Differentially Private Convex Optimization: Faster and More General
Andrew Lowy, Meisam Razaviyayn
Finding efficient, easily implementable differentially private (DP) algorithms that offer strong excess risk bounds is an important problem in modern machine learning. To date, mos…
Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems
Dmitrii M. Ostrovskii, Andrew Lowy, Meisam Razaviyayn
We propose an efficient algorithm for finding first-order Nash equilibria in min-max problems of the form , where the objective function is smoo…