Talker: Taiji Suzuki (TIT)
Title: Risk bounds of convex and Bayes tensor estimators: near
optimal rate without strong convexity
Abstract: Low rank tensor estimation is a useful statistical tool to
analyze multi-dimensional array data such as spatio-temporal data
and purchase data. We develop two types of estimators for estimating
a low rank tensor: minimum convex regularized risk minimizer and
Bayes estimator. We show the statistical convergence rates of these
methods. In particular, the Bayes estimator achieves near optimal
rate without restricted strong convexity on the design. This talk
partly includes collaborations with Ryota Tomioka.