Strategic Classification from Revealed Preferences
This addresses the challenge of robust classification in strategic environments, such as fraud detection or credit scoring, where agents adaptively game the system, offering a novel solution with theoretical guarantees.
The paper tackles the problem of online linear classification with strategic agents who manipulate their features to influence classification outcomes, proposing a computationally efficient algorithm that achieves diminishing Stackelberg regret, ensuring near-optimal performance compared to the best classifier in hindsight despite agent manipulation.
We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome. In rounds, the learner deploys a classifier, and an adversarially chosen agent arrives, possibly manipulating her features to optimally respond to the learner. The learner has no knowledge of the agents' utility functions or "real" features, which may vary widely across agents. Instead, the learner is only able to observe their "revealed preferences" --- i.e. the actual manipulated feature vectors they provide. For a broad family of agent cost functions, we give a computationally efficient learning algorithm that is able to obtain diminishing "Stackelberg regret" --- a form of policy regret that guarantees that the learner is obtaining loss nearly as small as that of the best classifier in hindsight, even allowing for the fact that agents will best-respond differently to the optimal classifier.