Stackelberg games for adversarial prediction problems
Stackelberg games for adversarial prediction problems
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DOI：
10.1145/2020408.2020495
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发表时间：
2011-08
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影响因子：
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通讯作者：
Michael Brückner;T. Scheffer
中科院分区：
文献类型：
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作者：
Michael Brückner;T. Scheffer
当根据预测模型生成测试数据时，违反了相同分布培训和测试数据的标准假设。例如，在电子邮件垃圾邮件过滤的背景下，这是显而易见的，电子邮件服务提供商使用垃圾邮件过滤器，而垃圾邮件发送者可以在生成新电子邮件时考虑此过滤器。我们将学习者与数据生成器之间的相互作用建模为Stackelberg竞争，其中学习者在领导者的角色中扮演着领导者的角色，并且数据生成器可能会对领导者的举动做出反应。我们得出了一个优化问题来确定该游戏的解决方案，并介绍了Stackelberg预测游戏的几个实例。我们表明，Stackelberg预测游戏概括了现有的预测模型。最后，我们在电子邮件垃圾邮件过滤的背景下经验地探索了讨论模型的属性。
The standard assumption of identically distributed training and test data is violated when test data are generated in response to a predictive model. This becomes apparent, for example, in the context of email spam filtering, where an email service provider employs a spam filter and the spam sender can take this filter into account when generating new emails. We model the interaction between learner and data generator as a Stackelberg competition in which the learner plays the role of the leader and the data generator may react on the leader's move. We derive an optimization problem to determine the solution of this game and present several instances of the Stackelberg prediction game. We show that the Stackelberg prediction game generalizes existing prediction models. Finally, we explore properties of the discussed models empirically in the context of email spam filtering.