Learning Temporal Point Processes via Reinforcement Learning

Understand

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

• The generative processes of these event data can be very complex, requiring flexible models to capture their dynamics.
• Temporal point processes offer an elegant framework for modeling event data without discretizing the time.
• However, the existing maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting the intensity function beforehand and cannot directly monitor the goodness-of-fit of the estimated model in the process of training.