Strategic Classification Made Practical

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.