论文标题
一般的战略分类和一致激励措施的情况
Generalized Strategic Classification and the Case of Aligned Incentives
论文作者
论文摘要
战略分类研究在环境中学习的学习，在这种情况下，自我利益的用户可以战略性地修改其功能以获得有利的预测结果。但是，一个关键的工作假设是，“有利”总是意味着“积极”。这在某些应用程序中可能是适当的（例如，贷款批准），但可以降低到对用户利益的相当狭窄的看法。在这项工作中，我们提出了更广泛的观点，即说明哪些涉及战略用户行为，并提出并研究了广义战略分类的灵活模型。我们的广义模型包括大多数当前模型，但包括其他新颖的设置；其中，我们确定并针对一个有趣的子类问题，其中用户和系统的利益是一致的。这种设置揭示了一个令人惊讶的事实：标准最大利润损失不适合战略投入。回到我们完全概括的模型时，我们提出了一个新颖的最大利润率框架，用于实用和有效，并从理论上分析。我们以一系列实验证明了我们方法的实用性。
Strategic classification studies learning in settings where self-interested users can strategically modify their features to obtain favorable predictive outcomes. A key working assumption, however, is that "favorable" always means "positive"; this may be appropriate in some applications (e.g., loan approval), but reduces to a fairly narrow view of what user interests can be. In this work we argue for a broader perspective on what accounts for strategic user behavior, and propose and study a flexible model of generalized strategic classification. Our generalized model subsumes most current models but includes other novel settings; among these, we identify and target one intriguing sub-class of problems in which the interests of users and the system are aligned. This setting reveals a surprising fact: that standard max-margin losses are ill-suited for strategic inputs. Returning to our fully generalized model, we propose a novel max-margin framework for strategic learning that is practical and effective, and which we analyze theoretically. We conclude with a set of experiments that empirically demonstrate the utility of our approach.
