ORIE Colloquium: Sarah Dean (Cornell CS)

Learning and decision-making in the presence of observer effects In many modern engineering domains, the presence of "observer effects" creates interdependence between measurement and underlying state. In such settings, actions both impact the system state and determine what information about it is observed. Accounting for this dual role is crucial for designing reliable algorithms for learning and control, for applications ranging from robotics to personalized recommendation systems. In this talk, I will discuss recent work in the setting of partially observed dynamical systems with linear st