Neural marked temporal point processes have been a valuable addition to the existing toolbox of statistical parametric models for continuous-time event data. These models are useful for sequences where each event is associated with a single item (a single type of event or a "mark") -- but such models are not suited for the practical situation where each event is associated with a set of items. In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model. In addition, we develop inference methods that can use such models to answer probabilistic queries such as "the probability of item $A$ being observed before item $B$," conditioned on sequence history. Computing exact answers for such queries is generally intractable for neural models due to both the continuous-time nature of the problem setting and the combinatorially-large space of potential outcomes for each event. To address this, we develop a class of importance sampling methods for querying with set-based sequences and demonstrate orders-of-magnitude improvements in efficiency over direct sampling via systematic experiments with four real-world datasets. We also illustrate how to use this framework to perform model selection using likelihoods that do not involve one-step-ahead prediction.

翻译：神经标记时间点过程已成为连续时间事件数据统计参数模型工具箱中的重要补充。这类模型适用于每个事件仅关联单个项目（即单一事件类型或"标记"）的序列，但无法处理每个事件关联一个项目集合的实际场景。本研究构建了一个通用的连续时间集合值数据建模框架，该框架兼容任何基于强度的循环神经点过程模型。此外，我们开发了推理方法，可利用此类模型回答诸如"在序列历史条件下，项目$A$在项目$B$之前被观测到的概率"等概率查询。由于问题设置的连续时间特性以及每个事件潜在结果的组合爆炸规模，神经模型通常无法精确计算此类查询。为解决此问题，我们提出了一类针对集合序列查询的重要性采样方法，并通过四个真实数据集的系统性实验证明，该方法在效率上比直接采样提升数个数量级。我们还演示了如何利用该框架进行模型选择——通过不涉及一步预测的似然函数实现。