9 citations · 17 across the 5 of their papers we have counts for
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cs.LG2022
On Private Online Convex Optimization: Optimal Algorithms in -Geometry and High Dimensional Contextual Bandits
Yuxuan Han, Zhicong Liang, Zhipeng Liang +3
Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streamin…
cs.LG2022
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
Donghao Li, Yang Cao, Yuan Yao
Privacy-preserving data release algorithms have gained increasing attention for their ability to protect user privacy while enabling downstream machine learning tasks. However, the…
cs.LG2020★ 9 cited
Differentially Private Federated Learning with Laplacian Smoothing
Zhicong Liang, Bao Wang, Quanquan Gu +2
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the p…