979 citations · 1k across the 15 of their papers we have counts for
9 papers · 1 filter
A Finite-Sample Analysis of Quantile Temporal-Difference Learning
Zijie Cheng, Xiang Li, Yang Peng +1
Quantile temporal-difference learning (QTD) is an effective method for learning return distributions through quantile approximation, yet its finite-time behavior remains poorly und…
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
Xiang Li, Wenhao Yang, Jiadong Liang +2
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings. Under a Lipschitz condition, we establish a functiona…
Statistical Estimation and Inference via Local SGD in Federated Learning
Xiang Li, Jiadong Liang, Xiangyu Chang +1
Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decen…
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
Xiao Guo, Xiang Li, Xiangyu Chang +2
Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine…
Communication-Efficient Distributed SVD via Local Power Iterations
Xiang Li, Shusen Wang, Kun Chen +1
We study distributed computing of the truncated singular value decomposition problem. We develop an algorithm that we call \texttt{LocalPower} for improving communication efficienc…
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.…