  We refine and generalize what is known about coresets for classification
problems via the sensitivity sampling framework. Such coresets seek the
smallest possible subsets of input data, so one can optimize a loss function on
the coreset and ensure approximation guarantees with respect to the original
data. Our analysis provides the first no dimensional coresets, so the size does
not depend on the dimension. Moreover, our results are general, apply for
distributional input and can use iid samples, so provide sample complexity
bounds, and work for a variety of loss functions. A key tool we develop is a
Radamacher complexity version of the main sensitivity sampling approach, which
can be of independent interest.
