  This paper uncovers and explores the close relationship between Monte Carlo
Optimization of a parametrized integral (MCO), Parametric machine-Learning
(PL), and `blackbox' or `oracle'-based optimization (BO). We make four
contributions. First, we prove that MCO is mathematically identical to a broad
class of PL problems. This identity potentially provides a new application
domain for all broadly applicable PL techniques: MCO. Second, we introduce
immediate sampling, a new version of the Probability Collectives (PC) algorithm
for blackbox optimization. Immediate sampling transforms the original BO
problem into an MCO problem. Accordingly, by combining these first two
contributions, we can apply all PL techniques to BO. In our third contribution
we validate this way of improving BO by demonstrating that cross-validation and
bagging improve immediate sampling. Finally, conventional MC and MCO procedures
ignore the relationship between the sample point locations and the associated
values of the integrand; only the values of the integrand at those locations
are considered. We demonstrate that one can exploit the sample location
information using PL techniques, for example by forming a fit of the sample
locations to the associated values of the integrand. This provides an
additional way to apply PL techniques to improve MCO.
