In this paper, we propose the first re-sampling based algorithm that is asymptotically optimal for several classes of possibly un-bounded parametric distributions.

In this paper, we propose the first re-sampling based algorithm that is asymptotically optimal for several classes of possibly un-bounded parametric distributions.

Anchorbased strategies have been treated as effective ways to alleviate such efficiency problems by propagation on representative entities instead of the whole graph.

Moreover, the problem of finding worst-case perturbations is non-convex and underparameterized, both ofwhich engender anon-favorable optimization landscape.