Computing Strategic Responses to Non-Linear Classifiers
This addresses a key limitation in strategic classification for scenarios requiring non-linear models, though it is incremental as it builds on existing linear approaches.
The paper tackles the problem of strategic classification with non-linear classifiers by introducing a method to compute best responses via Lagrangian dual optimization, demonstrating its applicability for evaluation and training in non-linear settings.
We consider the problem of strategic classification, where the act of deploying a classifier leads to strategic behaviour that induces a distribution shift on subsequent observations. Current approaches to learning classifiers in strategic settings are focused primarily on the linear setting, but in many cases non-linear classifiers are more suitable. A central limitation to progress for non-linear classifiers arises from the inability to compute best responses in these settings. We present a novel method for computing the best response by optimising the Lagrangian dual of the Agents' objective. We demonstrate that our method reproduces best responses in linear settings, identifying key weaknesses in existing approaches. We present further results demonstrating our method can be straight-forwardly applied to non-linear classifier settings, where it is useful for both evaluation and training.