Variational Policy for Guiding Point Processes

2017-01-30 · ICML 2017 8 · Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song

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.

📄 PDF Abstract BibTeX arXiv:1701.08585

Code (0)

등록된 구현이 없습니다.

Tasks

Point ProcessesVariational Inference

Variational Inference for Neyman-Scott Processes

2023-03-07 · Chengkuan Hong, Christian R. Shelton

Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters. Markov chain Monte Carlo (MCMC) is typically used for posterior sampling in the mo…

Point Processes Variational Inference

Deep Sigma Point Processes

2020-02-21 · Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs) retain many of the attractive features o…

Gaussian Processes Point Processes regression

Sparse Orthogonal Variational Inference for Gaussian Processes

2019-10-23 · pproximateinference AABI Symposium 2019 12 · Jiaxin Shi, Michalis K. Titsias, andriy mnih

We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods. It is based on decomposing a Gaussian…

Gaussian Processes Multi-class Classification regression Variational Inference

Variational Gaussian Process Diffusion Processes

2023-06-03 · Prakhar Verma, Vincent Adam, Arno Solin

Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilistic inference and learning under generat…

Variational Inference

Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks

2019-01-23 · Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir

Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detecti…