Abstract: Statistical inference of large matrices arises frequently in the analysis of massive datasets such as principle component analysis, matrix completion, functional genomics, etc. In this talk I introduce a new machinery for studying estimation of high-dimensional matrices, which yields tight non-asymptotic minimax rates for a large collection of loss functions in a variety of problems....