Generalization Analysis for Game-Theoretic Machine Learning

9 Oct 2014 Haifang Li Fei Tian Wei Chen Tao Qin Tie-Yan Liu

For Internet applications like sponsored search, cautions need to be taken when using machine learning to optimize their mechanisms (e.g., auction) since self-interested agents in these applications may change their behaviors (and thus the data distribution) in response to the mechanisms. To tackle this problem, a framework called game-theoretic machine learning (GTML) was recently proposed, which first learns a Markov behavior model to characterize agents' behaviors, and then learns the optimal mechanism by simulating agents' behavior changes in response to the mechanism... (read more)

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