Author

Xiang Li

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Preprint Sep 2026

Weak Convergence Rates for Partial-Sum Processes of Nonlinear Stochastic Approximation

Stochastic approximation provides a general framework for online estimation and optimization. Statistical inference based on the resulting estimates requires understanding their fluctuations around the target. For Polyak--Ruppert averaging, functional central limit theorems describe the normalized cumulative estimation...

Xiang Li, Jia-Dong Liang, Zhi-Hua Zhang · 0 citations

Preprint Aug 2026

A Finite-Sample Analysis of Quantile Temporal-Difference Learning

Quantile temporal-difference learning (QTD) is an effective method for learning return distributions through quantile approximation, yet its finite-time behavior remains poorly understood. Its update is nonlinear and nonsmooth, and the stability needed for a sharp convergence rate holds only near the target. We establi...

Zijie Cheng, Xiang Li, Yang Peng et al. · 0 citations

#machine learning Preprint Aug 2026

A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning. The proof separates two stability mechanisms. A global comparison argument, based on the order monotonicity of reward cumulative distribution functions and the $W_\...

Zijie Cheng, Xiang Li, Yang Peng et al. · 0 citations