Variational Neuro-Symbolic Generative Temporal Point Process

Chao Yang, Yang Yang, Wendi Ren, Shuang Li

Keywords: temporal point process, neuro-symbolic, generative model

Abstract:

Temporal point processes (TPPs) are a powerful framework for modeling event sequences with irregular timestamps, such as those commonly found in electronic health records (EHR), which often involve high-dimensional and diverse event types. However, building generative models for such complex datasets comes with several challenges, including addressing sample inefficiency, accurately capturing intricate event patterns, and producing outputs that are both trustworthy and interpretable. In this paper, we present a neuro-symbolic generative model for TPPs based on the Variational Autoencoder (VAE) framework. Our model incorporates a neural-symbolic reasoning layer into the latent space, allowing it to integrate interpretable, logic-based constraints and perform logical reasoning over learned representations. This integration enhances the interpretability of the latent space by embedding logic rules directly into the generative process, enabling structured reasoning and improved decision-making based on underlying data patterns. We validate our model on an ICU EHR dataset, demonstrating its effectiveness in capturing complex event dynamics with irregular timestamps. In addition to improving sample efficiency and accuracy, our model supports the secure and interpretable generation of synthetic event data, making it a valuable tool for healthcare applications where reliability and trustworthiness are critical.

Primary Area: generative models

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Submission Number: 13231