Georgia Institute of Technology College of Sciences

In this continuing joint work with Benjamin Arras, we explore connections between covariance representations and Stein's method. In particular,  via Stein's kernels we obtain quantitative high-dimensional CLTs in 1-Wasserstein distance when the limiting Gaussian probability measure is anisotropic. The dependency on the parameters is completely explicit and the rates of convergence are sharp.