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库中的重要补充。这类模型适用于每个事件关联单一项目（单一事件类型或“标记”）的序列——但此类模型并不适用于每个事件关联一组项目的实际场景。本研究发展了一个通用框架，用于对连续时间中的集合值数据进行建模，该框架与任何基于强度的递归神经点过程模型兼容。此外，我们还开发了推理方法，可利用此类模型回答诸如“项目A在项目B之前被观测到的概率”等概率查询（基于序列历史条件）。由于问题场景的连续时间特性以及每个事件可能结果的组合爆炸空间，对于神经模型而言，精确计算此类查询通常是棘手的。为解决此问题，我们针对集合序列查询开发了一类重要性采样方法，并通过在四个真实世界数据集上的系统性实验证明，该方法相比直接采样在效率上实现了数量级提升。我们还展示了如何利用该框架执行模型选择，其使用不涉及单步预测的似然函数。