Michael Jordan, University of California, Berkeley
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, human-focused datasets, in data-analytic contexts such as classifiers and recommendation systems. I'll discuss several recent projects that aim to explore this interface: (1) exploration-exploitation tradeoffs for bandits that compete over a scarce resource, (2) gradient-based algorithms that find Nash equilibria, and only Nash equilibria; (3) notions of local optimality in nonconvex-nonconcave minimax optimization, and how such notions relate to stochastic gradient methods; and (4) economic perspectives on online control of false-discovery rates.
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. Prof. Jordan is a member of the National Academy of Sciences and a member of the National Academy of Engineering. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics, and he has given a Plenary Lecture at the International Congress of Mathematicians. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009.