  Recently, there is a surge of interest in using point processes to model
continuous-time user activities. This framework has resulted in novel models
and improved performance in diverse applications. However, most previous works
focus on the "open loop" setting where learned models are used for predictive
tasks. Typically, we are interested in the "closed loop" setting where a policy
needs to be learned to incorporate user feedbacks and guide user activities to
desirable states. Although point processes have good predictive performance, it
is not clear how to use them for the challenging closed loop activity guiding
task. In this paper, we propose a framework to reformulate point processes into
stochastic differential equations, which allows us to extend methods from
stochastic optimal control to address the activity guiding problem. We also
design an efficient algorithm, and show that our method guides user activities
to desired states more effectively than the state of the art.
