5 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…
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 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…