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Nash Equilibria of Static Prediction Games
Michael Brückner · Tobias Scheffer

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @ None #None

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.

Author Information

Michael Brückner (SoundCloud Ltd.)
Tobias Scheffer (Universität Potsdam)

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