16 citations · 74 across the 27 of their papers we have counts for
5 papers · 1 filter
Zero redundancy distributed learning with differential privacy
Zhiqi Bu, Justin Chiu, Ruixuan Liu +2
Deep learning using large models have achieved great success in a wide range of domains. However, training these models on billions of parameters is very challenging in terms of th…
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu +1
Differentially Private Stochastic Gradient Descent with Gradient Clipping (DPSGD-GC) is a powerful tool for training deep learning models using sensitive data, providing both a sol…
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang +2
Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language mode…
Coupling public and private gradient provably helps optimization
Ruixuan Liu, Zhiqi Bu, Yu-xiang Wang +2
The success of large neural networks is crucially determined by the availability of data. It has been observed that training only on a small amount of public data, or privately on…
MISNN: Multiple Imputation via Semi-parametric Neural Networks
Zhiqi Bu, Zongyu Dai, Yiliang Zhang +1
Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream da…