979 citations · 1.5k across the 30 of their papers we have counts for
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Fast Generalized Matrix Regression with Applications in Machine Learning
Haishan Ye, Shusen Wang, Zhihua Zhang +1
Fast matrix algorithms have become the fundamental tools of machine learning in big data era. The generalized matrix regression problem is widely used in the matrix approximation s…
Communication-Efficient Local Decentralized SGD Methods
Xiang Li, Wenhao Yang, Shusen Wang +1
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it…
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping
Shusen Wang
Random feature mapping (RFM) is a popular method for speeding up kernel methods at the cost of losing a little accuracy. We study kernel ridge regression with random feature mappin…
Matrix Sketching for Secure Collaborative Machine Learning
Mengjiao Zhang, Shusen Wang
Collaborative learning allows participants to jointly train a model without data sharing. To update the model parameters, the central server broadcasts model parameters to the clie…
On the Convergence of FedAvg on Non-IID Data
Xiang Li, Kaixuan Huang, Wenhao Yang +2
Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading algorithm in this setting, Federated Averaging (\tex…
Do Subsampled Newton Methods Work for High-Dimensional Data?
Xiang Li, Shusen Wang, Zhihua Zhang
Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.…