Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation

The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable $Yinmathbb{R}$ given another random variable $Xinmathbb{R}^{d}$ is of much interest in a variety of statistical contexts. In this paper we propose two estimators for the MMSE, one based on a two-layer neural network and the other on a special three-layer neural network. We derive lower bounds for the MMSE based on the proposed estimators and the Barron constant of an appropriate function of the conditional expectation of $Y$ given $X$. Furthermore, we derive a general upper bound for the Barron constant that, when $Xinmathbb{R}$ is post-processed by the additive Gaussian mechanism, produces order optimal estimates in the large noise regime.