NIPS Proceedingsβ

Nash Equilibria of Static Prediction Games

Part of: Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Authors

Abstract

The standard assumption of identically distributed training and test data can be violated when an adversary can exercise some control over the generation of the test data. In a prediction game, a learner produces a predictive model while an adversary may alter the distribution of input data. We study single-shot prediction games in which the cost functions of learner and adversary are not necessarily antagonistic. We identify conditions under which the prediction game has a unique Nash equilibrium, and derive algorithms that will find the equilibrial prediction models. In a case study, we explore properties of Nash-equilibrial prediction models for email spam filtering empirically.