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February 9 @ 4:00 pm - 5:00 pm
Title: Statistical Game Theory
Presenter: Arun Sai Suggala CMU
Abstract: Game theory and statistics are often regarded as disparate research areas. This is because typical statistical estimation settings are non-adversarial, and the samples are assumed to be generated by some stationary non-reactive source. However, there is a great degree of commonality between the two fields. Classically, the mathematical philosophy of statistics, particularly frequentist statistics, posits that the source of samples is potentially adversarial. This resulted in the rich theory of minimax statistical games and estimation. Boosting algorithms, which are often regarded as best off-the-shelf classifiers, can be viewed as playing a zero-sum game against a weak learner. To allow for various departures of “test environment” from “train environments”, the emerging field of robust machine learning allows for adversarial manipulation of the train or test environments. Finally, an emerging class of density estimators in modern machine learning use an adversarial “critic” of the density estimator to improve the final density estimation. The common theme among these classical and modern developments is an interplay between statistical estimation and two or multiplayer games. In this talk, I will present some of my recent work at the intersection of statistics and game theory and show how game theory can advance statistics.