Other Event

Decentralized online learning: decision making under uncertainty and with crowdsourced data

Mingyan LiuProfessorUniversity of Michigan, Department of Electrical Engineering & Computer Science

In this talk I will discuss a type of learning, referred to as regret learning, commonly used in dealing with decision making under uncertainty in the environment, which may also include other similar users. A common feature shared by this type of learning algorithms is to use a combination of "exploration" , where the user samples different options to find out good actions to take, and "exploitation" , where the user takes what he/she believes to be good actions to maximize his/her gain. The design of a good algorithm boils down to determining when to explore, when to exploit and how. This is made more complex when there are multiple uncoordinated such users present in the system. I will go through a few example algorithms and use a number of applications to motivate and illustrate this learning process, including resource allocation in wireless networks and building better classifiers using crowdsourced data.

Sponsored by

University of Michigan, Department of Electrical Engineering & Computer Science