2 citations · 2 across the 4 of their papers we have counts for
4 papers
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
Yixuan Liu, Li Xiong, Yuhan Liu +3
Differentially Private Stochastic Gradients Descent (DP-SGD) is a prominent paradigm for preserving privacy in deep learning. It ensures privacy by perturbing gradients with random…
The role of shared randomness in quantum state certification with unentangled measurements
Yuhan Liu, Jayadev Acharya
Given copies of an unknown quantum state , quantum state certification is the task of determining whether or , wher…
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
Yixuan Liu, Suyun Zhao, Li Xiong +2
Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfie…
Learning to Rearrange with Physics-Inspired Risk Awareness
Meng Song, Yuhan Liu, Zhengqin Li +1
Real-world applications require a robot operating in the physical world with awareness of potential risks besides accomplishing the task. A large part of risky behaviors arises fro…