2 citations · 8 across the 12 of their papers we have counts for
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cs.LG2022★ 1 cited
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…
cs.LG2022★ 1 cited
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…
cs.LG2022
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…