Strategic Classification Made Practical
Sagi Levanon, Nir Rosenfeld

TL;DR
This paper introduces a practical learning framework for strategic classification that minimizes strategic empirical risk by differentiating through user responses, enabling more realistic and flexible learning scenarios.
Contribution
It presents a novel, practical approach to strategic classification that directly minimizes strategic empirical risk, extending beyond theoretical models.
Findings
Effective in various learning settings
Outperforms traditional methods in strategic environments
Demonstrates flexibility and practicality
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 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.
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Taxonomy
TopicsMachine Learning and Algorithms · Data Stream Mining Techniques · Imbalanced Data Classification Techniques
