Strategic Classification with Unknown User Manipulations

This work proposes a novel batch-learning setting in which it is possible to learn a close to optimal classifier in terms of the strategic loss even without knowing the feasible manipulations beforehand, and does not assume a best-response from agents but only require that observed manipulations are feasible.