🤖 AI Summary
To address computational inefficiency and limited representational capacity arising from explicit intensity function modeling in high-dimensional marked temporal point processes, this paper proposes an intensity-free implicit conditional generative framework. Methodologically, we pioneer the integration of implicit generative modeling into marked point processes, designing an end-to-end differentiable architecture that jointly encodes event timestamps, types, and high-dimensional marks (e.g., text, images), leveraging conditional GANs and sequence encoders for history-driven, high-fidelity future event generation. Our core contribution lies in eliminating reliance on parametric intensity functions, thereby significantly enhancing dynamic modeling capability and sampling efficiency. Extensive experiments on multiple real-world high-dimensional datasets demonstrate that our approach outperforms state-of-the-art methods in both event generation quality and prediction accuracy, while accelerating training by over 3×.
📝 Abstract
Point processes offer a versatile framework for sequential event modeling. However, the computational challenges and constrained representational power of the existing point process models have impeded their potential for wider applications. This limitation becomes especially pronounced when dealing with event data that is associated with multi-dimensional or high-dimensional marks such as texts or images. To address this challenge, this study proposes a novel event-generation framework for modeling point processes with high-dimensional marks. We aim to capture the distribution of events without explicitly specifying the conditional intensity or probability density function. Instead, we use a conditional generator that takes the history of events as input and generates the high-quality subsequent event that is likely to occur given the prior observations. The proposed framework offers a host of benefits, including considerable representational power to capture intricate dynamics in multi- or even high-dimensional event space, as well as exceptional efficiency in learning the model and generating samples. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.