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.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-wang17k, title = {Variational Policy for Guiding Point Processes}, author = {Yichen Wang and Grady Williams and Evangelos Theodorou and Le Song}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {3684--3693}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/wang17k/wang17k.pdf}, url = {https://proceedings.mlr.press/v70/wang17k.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Variational Policy for Guiding Point Processes %A Yichen Wang %A Grady Williams %A Evangelos Theodorou %A Le Song %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-wang17k %I PMLR %P 3684--3693 %U https://proceedings.mlr.press/v70/wang17k.html %V 70 %X 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.
APA
Wang, Y., Williams, G., Theodorou, E. & Song, L.. (2017). Variational Policy for Guiding Point Processes. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:3684-3693 Available from https://proceedings.mlr.press/v70/wang17k.html.

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