979 citations · 1k across the 15 of their papers we have counts for
17 papers
Weak Convergence Rates for Partial-Sum Processes of Nonlinear Stochastic Approximation
Xiang Li, Jiadong Liang, Zhihua Zhang
Stochastic approximation provides a general framework for online estimation and optimization. Statistical inference based on the resulting estimates requires understanding their fl…
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…
Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
Yuze Han, Xiang Li, Jiadong Liang +1
In two-time-scale stochastic approximation (SA), two iterates are updated at different rates, governed by distinct step sizes, with each update influencing the other. Previous stud…
Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
Yuze Han, Xiang Li, Zhihua Zhang
In two-time-scale stochastic approximation (SA), two iterates are updated at varying speeds using different step sizes, with each update influencing the other. Previous studies on…
Convergence and Inference of Stream SGD, with Applications to Queueing Systems and Inventory Control
Xiang Li, Jiadong Liang, Xinyun Chen +1
Stream stochastic gradient descent (SGD) is a simple and efficient method for solving online optimization problems in operations research (OR), where data is generated by parameter…
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Jiadong Liang, Yuze Han, Xiang Li +1
In this paper we consider linearly constrained optimization problems and propose a loopless projection stochastic approximation (LPSA) algorithm. It performs the projection with pr…
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
Xiang Li, Qiang Sun
This paper presents two algorithms, AdaOFUL and VARA, for online sequential decision-making in the presence of heavy-tailed rewards with only finite variances. For linear stochasti…
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data
Xiang Li, Jiadong Liang, Zhihua Zhang
Many stochastic approximation (SA) algorithms evolve along a single trajectory, making uncertainty quantification challenging under nonlinear dynamics and Markov dependence. We dev…
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…
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 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.…