On gradient-based methods for finding game-theoretic equilibria
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. The aim is to blend gradient-based methodology with game-theoretic goals as part of a large “microeconomics meets machine learning” program.
Michael Jordan details several recent results, including how to define local optimality in nonconvex-nonconcave minimax optimization and how such a definition relates to stochastic gradient methods; a gradient-based algorithm that finds Nash equilibria, and only Nash equilibria; and exploration-exploitation trade-offs for bandits involving competition over a scarce resource.
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.
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