8 citations · 10 across the 10 of their papers we have counts for
11 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…
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…
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…
Gradient Tracking for High Dimensional Federated Optimization
Jiadong Liang, Yang Peng, Zhihua Zhang
In this paper, we study the (decentralized) distributed optimization problem with high-dimensional sparse structure. Building upon the FedDA algorithm, we propose a (Decentralized)…
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…
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…
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
Liangyu Zhang, Yang Peng, Wenhao Yang +1
We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularl…
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…
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…
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…
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
Zhiming Zhou, Yuxuan Song, Lantao Yu +5
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs. We study the property of the optimal discriminative function and show…