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.

连续时间中的神经标记时间点过程为连续时间事件数据的统计参数模型工具箱增添了有价值的内容。本文提出了一个通用框架，用于建模连续时间中的集合值数据，并开发了适用于任何基于强调的递归神经点过程模型的推理方法。在四个真实世界数据集上进行系统实验，通过对基于集合的序列进行重要性抽样，证明了效率大幅提升。通过展示如何使用该框架来执行不涉及一步预测的似然度进行模型选择。

连续时间下集合序列的概率建模

Probabilistic Modeling for Sequences of Sets in Continuous-Time

Autoregressive neural networks within the temporal point process (TPP)
framework have become the standard for modeling continuous-time event data.
Even though these models can expressively capture event sequences in a
one-step-ahead fashion, they are inherently limited for long-term forecasting
applications due to the accumulation of errors caused by their sequential
nature. To overcome these limitations, we derive ADD-THIN, a principled
probabilistic denoising diffusion model for TPPs that operates on entire event
sequences. Unlike existing diffusion approaches, ADD-THIN naturally handles
data with discrete and continuous components. In experiments on synthetic and
real-world datasets, our model matches the state-of-the-art TPP models in
density estimation and strongly outperforms them in forecasting.

在连续时间事件数据的时间点过程（TPP）框架中，自回归神经网络已成为建模连续时间事件数据的标准。为了克服这些限制，我们推导出 ADD-THIN，一种基于整个事件序列的原则性概率去噪扩散模型，它与现有的扩散方法不同，可以自然地处理具有离散和连续分量的数据。在合成和真实数据集的实验中，我们的模型在密度估计方面与最先进的 TPP 模型相匹配，并在预测方面明显优于它们。