  Recent work introduced the model of learning from discriminative feature
feedback, in which a human annotator not only provides labels of instances, but
also identifies discriminative features that highlight important differences
between pairs of instances. It was shown that such feedback can be conducive to
learning, and makes it possible to efficiently learn some concept classes that
would otherwise be intractable. However, these results all relied upon perfect
annotator feedback. In this paper, we introduce a more realistic, robust
version of the framework, in which the annotator is allowed to make mistakes.
We show how such errors can be handled algorithmically, in both an adversarial
and a stochastic setting. In particular, we derive regret bounds in both
settings that, as in the case of a perfect annotator, are independent of the
number of features. We show that this result cannot be obtained by a naive
reduction from the robust setting to the non-robust setting.
