提出新方法计算非线性分类器下的最优策略响应，解决部署后数据分布偏移问题。
Computing Strategic Responses to Non-Linear Classifiers
- 通过优化代理目标的对偶拉格朗日函数，实现最佳策略响应计算
- 在非线性分类器上成功应用，可直接用于模型评估与训练
- 揭示现有线性方法缺陷，为复杂场景下公平决策提供工具
我们研究战略分类问题：部署分类器会引发个体策略性行为，导致后续观测分布发生变化。现有方法主要集中在线性情形，但许多实际场景更适合使用非线性分类器。当前进展的核心障碍在于无法有效计算非线性设置下的最优响应。本文提出一种新方法，通过优化代理目标的拉格朗日对偶，实现最佳响应的计算。实验表明该方法在线性场景中能准确复现最优响应，并暴露出现有方法的关键缺陷。进一步结果证明，该方法可直接应用于非线性分类器场景，对模型评估和训练均具实用性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。