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.

翻译：点过程为序列事件建模提供了灵活的框架。然而，现有各类点过程模型面临计算挑战且表征能力受限，这阻碍了它们在更广泛场景中的应用潜力。当处理与多维或高维标记（如文本或图像）相关联的事件数据时，此类局限性尤为突出。为解决这一难题，本文提出一种面向高维标记点过程建模的新型事件生成框架。我们旨在无需显式指定条件强度或概率密度函数的情况下捕获事件分布，转而采用条件生成器：该生成器以历史事件序列为输入，基于先验观测生成后续最可能发生的高质量事件。所提框架具备多重优势，包括在多维甚至高维事件空间中捕获复杂动态的显著表征能力，以及在模型学习与样本生成过程中展现的卓越效率。数值结果表明，本方法相较其他现有最优基线模型具有更优性能。