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- W3134759790 abstract "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 classification that is practical. Our approach directly minimizes the strategic empirical risk, achieved by differentiating through the 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." @default.
- W3134759790 created "2021-03-15" @default.
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- W3134759790 date "2021-03-02" @default.
- W3134759790 modified "2023-09-28" @default.
- W3134759790 title "Strategic Classification Made Practical" @default.
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