Local linear convergence of gradient methods for overparameterized Gaussian mixtures

ArXi:2605.30936v1 Announce Type: new We study the problem of learning Gaussian mixture models under overparameterization. Prior work has shown that while overparameterization is essential for avoiding spurious local optima and enables global recovery of the ground-truth model using the gradient-EM (expectation-maximization) algorithm, it can dramatically slow down the local rate of convergence.