Map direction to the entrance for seminar room
Speaker: Michael I Jordan, Department of EECS & Department of Statistics, University of California, Berkeley
Title: On Dynamics-Informed Blending of Machine Learning and Game Theory
Abstract:
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, and in data-analytic contexts
such as classifiers and recommendation systems. I'll discuss several recent projects
that aim to explore the interface between machine learning and microeconomics,
including leader/follower dynamics in strategic classification, the robust learning
of optimal auctions, and a Lyapunov theory for matching markets with transfers.
