Nonzero-sum Adversarial Hypothesis Testing Games
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Publication:6326150
arXiv1909.13031MaRDI QIDQ6326150
Author name not available (Why is that?)
Publication date: 28 September 2019
Abstract: We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rates of convergence of classification errors at equilibrium, which are analogous to the well-known Chernoff-Stein lemma and Chernoff information that describe the error exponents in the classical binary hypothesis testing problem, but with parameters derived from the adversarial model. The results are validated through numerical experiments.
Has companion code repository: https://github.com/sarath1789/ahtg_neurips2019
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