用最优传输正则化，让事件序列模型自动发现并保持结构聚类。
Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization
- 通过格罗莫夫-瓦瑟斯坦正则化，在最大似然框架中强制序列嵌入的聚类结构。
- 在大规模场景下采样核矩阵，实现计算效率与结构规整性的平衡。
- 模型预测准确率不降，却显著提升可解释性，适合需要结构洞察的任务。
真实世界中的事件序列通常由不同的时间点过程（TPPs）生成，具有天然的聚类结构。然而，现有大部分TPP模型忽略了事件序列的内在聚类特性，导致模型可解释性不足。本文提出一种基于格罗莫夫-瓦瑟斯坦（GW）正则化的结构增强型TPP方法，在最大似然估计框架中对序列级嵌入施加聚类结构约束。训练阶段，采用非参数TPP核来正则化基于序列嵌入构建的相似性矩阵；在大规模应用中，通过采样核矩阵，将正则化实现为一个GW差异项，兼顾结构规整性与计算效率。所学得的TPP模型产生聚类化的序列嵌入，在预测与聚类性能上均表现良好，显著提升模型可解释性，且未牺牲预测准确性。
原文摘要 · Abstract (English)
Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we learn structure-enhanced TPPs with the help of Gromov-Wasserstein (GW) regularization, which imposes clustering structures on the sequence-level embeddings of the TPPs in the maximum likelihood estimation framework.In the training phase, the proposed method leverages a nonparametric TPP kernel to regularize the similarity matrix derived based on the sequence embeddings. In large-scale applications, we sample the kernel matrix and implement the regularization as a Gromov-Wasserstein (GW) discrepancy term, which achieves a trade-off between regularity and computational efficiency.The TPPs learned through this method result in clustered sequence embeddings and demonstrate competitive predictive and clustering performance, significantly improving the model interpretability without compromising prediction accuracy.
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