2 citations · 2 across the 2 of their papers we have counts for
5 papers
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Wenhao Yang, Xiang Li, Guangzeng Xie +1
Regularized MDPs serve as a smooth version of original MDPs. However, biased optimal policy always exists for regularized MDPs. Instead of making the coefficientλof regularized ter…
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…
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…
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Xiang Li, Wenhao Yang, Zhihua Zhang
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total r…
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.…