研究非线性分类器下的策略性行为，发现其会显著改变模型复杂度。
Strategic Classification with Non-Linear Classifiers
- 从决策边界出发分析非线性分类器如何影响用户策略行为
- 发现策略行为可使有效类别复杂度下降甚至任意降低
- 证明神经网络等通用逼近器在策略环境中不再通用
在策略分类中，标准监督学习扩展为支持用户因分类器结果而进行成本高昂的特征操纵。尽管标准学习支持多种模型类别，但现有策略分类研究主要集中在线性分类器。本文通过自下而上的方法，研究非线性分类器下策略行为的表现及其对学习的影响。结果表明，与线性情形不同，策略行为可能增加或减少有效类别复杂度，且复杂度的下降可任意大。另一关键发现是，通用逼近器（如神经网络）在策略环境中不再具有通用性。我们实证展示了即使在无限制模型类中，这种特性也可能导致性能差距。
原文摘要 · Abstract (English)
In strategic classification, the standard supervised learning setting is extended to support the notion of strategic user behavior in the form of costly feature manipulations made in response to a classifier. While standard learning supports a broad range of model classes, the study of strategic classification has, so far, been dedicated mostly to linear classifiers. This work aims to expand the horizon by exploring how strategic behavior manifests under non-linear classifiers and what this implies for learning. We take a bottom-up approach showing how non-linearity affects decision boundary points, classifier expressivity, and model class complexity. Our results show how, unlike the linear case, strategic behavior may either increase or decrease effective class complexity, and that the complexity decrease may be arbitrarily large. Another key finding is that universal approximators (e.g., neural nets) are no longer universal once the environment is strategic. We demonstrate empirically how this can create performance gaps even on an unrestricted model class.
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