  Strategic classification regards the problem of learning in settings where
users can strategically modify their features to improve outcomes. This setting
applies broadly and has received much recent attention. But despite its
practical significance, work in this space has so far been predominantly
theoretical. In this paper we present a learning framework for strategic
classification that is practical. Our approach directly minimizes the
"strategic" empirical risk, achieved by differentiating through the strategic
response of users. This provides flexibility that allows us to extend beyond
the original problem formulation and towards more realistic learning scenarios.
A series of experiments demonstrates the effectiveness of our approach on
various learning settings.
