= Argmin consistency under uniform convergence in probability
On a compact parameter set, a continuous deterministic objective with a unique minimum has a positive separation gap outside each neighbourhood of its <minimizer>. <Uniform convergence in probability> of the random objectives bounds the deterministic objective difference at any attained random <minimizer> by twice the supremum discrepancy. This proves <statistical consistency> of each measurable minimizing selection.
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