  This paper extends standard results from learning theory with independent
data to sequences of dependent data. Contrary to most of the literature, we do
not rely on mixing arguments or sequential measures of complexity and derive
uniform risk bounds with classical proof patterns and capacity measures. In
particular, we show that the standard classification risk bounds based on the
VC-dimension hold in the exact same form for dependent data, and further
provide Rademacher complexity-based bounds, that remain unchanged compared to
the standard results for the identically and independently distributed case.
Finally, we show how to apply these results in the context of scenario-based
optimization in order to compute the sample complexity of random programs with
dependent constraints.
