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

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