4 papers
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…
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 Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
Chuhan Xie, Kaicheng Jin, Jiadong Liang +1
We study time-uniform statistical inference for parameters in stochastic approximation (SA), which encompasses a bunch of applications in optimization and machine learning. To that…
Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang +2
In this paper, we study distributional reinforcement learning from the perspective of statistical efficiency. We investigate distributional policy evaluation, aiming to estimate th…