7 papers
Connecting Score Matching, Maximum Likelihood, and Expectation-Maximization in Mixed Linear Regression
Zhankun Luo, Abolfazl Hashemi
We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statistical guarantees of score matc…
Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3
Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as…
RAMPAGE: RAndomized Mid-Point for debiAsed Gradient Extrapolation
Zhankun Luo, M. Berk Sahin, Antesh Upadhyay +2
A celebrated method for Variational Inequalities (VIs) is Extragradient (EG), which can be viewed as a standard discrete-time integration scheme. With this view in mind, in this pa…
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1
This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient m…
Structural Properties, Cycloid Trajectories and Non-Asymptotic Guarantees of EM Algorithm for Mixed Linear Regression
Zhankun Luo, Abolfazl Hashemi
This work investigates the structural properties, cycloid trajectories, and non-asymptotic convergence guarantees of the Expectation-Maximization (EM) algorithm for two-component M…
Characterizing Evolution in Expectation-Maximization Estimates for Overspecified Mixed Linear Regression
Zhankun Luo, Abolfazl Hashemi
Mixture models have attracted significant attention due to practical effectiveness and comprehensive theoretical foundations. A persisting challenge is model misspecification, whic…
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
Zhankun Luo, Abolfazl Hashemi
We study the trajectory of iterations and the convergence rates of the Expectation-Maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goa…