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On Theoretical Limits of Learning with Label Differential Privacy
Label differential privacy (DP) is designed for learning problems involving private labels and
public features. While various methods have been proposed for learning under label DP, the …
public features. While various methods have been proposed for learning under label DP, the …
Private least absolute deviations with heavy-tailed data
We study the problem of Differentially Private Stochastic Convex Optimization (DPSCO) with
heavy-tailed data. Specifically, we focus on the problem of Least Absolute Deviations, ie, ℓ 1 …
heavy-tailed data. Specifically, we focus on the problem of Least Absolute Deviations, ie, ℓ 1 …
A Stochastic Conjugate Subgradient Framework for Large-Scale Stochastic Optimization Problems
D Zhang - 2024 - search.proquest.com
Stochastic Optimization is a cornerstone of operations research, providing a framework to
solve optimization problems under uncertainty. Despite the development of numerous …
solve optimization problems under uncertainty. Despite the development of numerous …