Variational Policy for Guiding Point Processes


Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3684-3693, 2017.


Temporal point processes have been widely applied to model event sequence data generated by online users. In this paper, we consider the problem of how to design the optimal control policy for point processes, such that the stochastic system driven by the point process is steered to a target state. In particular, we exploit the key insight to view the stochastic optimal control problem from the perspective of optimal measure and variational inference. We further propose a convex optimization framework and an efficient algorithm to update the policy adaptively to the current system state. Experiments on synthetic and real-world data show that our algorithm can steer the user activities much more accurately and efficiently than other stochastic control methods.

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