  Tree ensembles are one of the most widely used model classes. However, these
models are susceptible to adversarial examples, i.e., slightly perturbed
examples that elicit a misprediction. There has been significant research on
designing approaches to construct such examples for tree ensembles. But this is
a computationally challenging problem that often must be solved a large number
of times (e.g., for all examples in a training set). This is compounded by the
fact that current approaches attempt to find such examples from scratch. In
contrast, we exploit the fact that multiple similar problems are being solved.
Specifically, our approach exploits the insight that adversarial examples for
tree ensembles tend to perturb a consistent but relatively small set of
features. We show that we can quickly identify this set of features and use
this knowledge to speedup constructing adversarial examples.
