  We consider maximization of stochastic monotone continuous submodular
functions (CSF) with a diminishing return property. Existing algorithms only
guarantee the performance \textit{in expectation}, and do not bound the
probability of getting a bad solution. This implies that for a particular run
of the algorithms, the solution may be much worse than the provided guarantee
in expectation. In this paper, we first empirically verify that this is indeed
the case. Then, we provide the first \textit{high-probability} analysis of the
existing methods for stochastic CSF maximization, namely PGA, boosted PGA, SCG,
and SCG++. Finally, we provide an improved high-probability bound for SCG,
under slightly stronger assumptions, with a better convergence rate than that
of the expected solution. Through extensive experiments on non-concave
quadratic programming (NQP) and optimal budget allocation, we confirm the
validity of our bounds and show that even in the worst-case, PGA converges to
$OPT/2$, and boosted PGA, SCG, SCG++ converge to $(1 - 1/e)OPT$, but at a
slower rate than that of the expected solution.
