Publications (8)
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 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…
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
Yuze Han, Guangzeng Xie, Zhihua Zhang
In this paper, we study the lower complexity bounds for finite-sum optimization problems, where the objective is the average of individual component functions. We consider Prox…
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…
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
Dachao Lin, Yuze Han, Haishan Ye +1
We study finite-sum distributed optimization problems involving a master node and local nodes under the popular -similarity and -strong convexity conditions. We propo…
Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits
Jingxin Zhan, Yuze Han, Zhihua Zhang
The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly important, as it captures the learn…