A note on $L^1$-Convergence of the Empiric Minimizer for unbounded functions with fast growth

For $V:{\mathbb{R}}^d\to \mathbb{R}$ coercive, we study the convergence rate for the $L^1$-distance of the empiric minimizer, which is the true minimum of the function $V$ sampled with noise with a finite number $n$ of samples, to the minimum of $V$. We show that in general, for unbounded functions with fast growth, the convergence rate is bounded above by $a_n{n}^{-1\mathrm{/}q}$, where $q$ is the dimension of the latent random variable and where $a_n=o(n^{\epsilon })$ for every $\epsilon >0$. We then present applications to optimization problems arising in Machine Learning and in Monte Carlo simulation.