论文标题
在一个状态世界中的表演预测
Performative Prediction in a Stateful World
论文作者
论文摘要
部署的监督机器学习模型做出了与世界互动和影响世界的预测。这种现象被Perdomo等人称为表演性预测。 （ICML 2020）。了解此类预测的影响以及设计工具以控制这种影响是一个持续的挑战。我们提出了一个理论框架，在该框架中，目标人群对部署的分类器的响应是根据分类器和人群的当前状态（分布）建模的。我们显示了与两种重复的风险最小化和懒惰变体的均衡相互收敛的必要条件。此外，收敛是接近最佳分类器。因此，我们概括了Perdomo等人的结果，Perdomo等人的性能框架不承担对目标人群状态的任何依赖。我们的模型捕获的一种特殊现象是以不同速率获取信息和资源的不同群体，以便能够响应最新的部署分类器。我们从理论和经验上研究了这一现象。
Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called performative prediction by Perdomo et al. (ICML 2020). It is an ongoing challenge to understand the influence of such predictions as well as design tools so as to control that influence. We propose a theoretical framework where the response of a target population to the deployed classifier is modeled as a function of the classifier and the current state (distribution) of the population. We show necessary and sufficient conditions for convergence to an equilibrium of two retraining algorithms, repeated risk minimization and a lazier variant. Furthermore, convergence is near an optimal classifier. We thus generalize results of Perdomo et al., whose performativity framework does not assume any dependence on the state of the target population. A particular phenomenon captured by our model is that of distinct groups that acquire information and resources at different rates to be able to respond to the latest deployed classifier. We study this phenomenon theoretically and empirically.
