提出新方法同时处理事件流中的遗漏与误增异常点。
Learning under Commission and Omission Event Outliers
- 用动态权重函数调整事件重要性，提升模型鲁棒性。
- 理论与实验均证明在分类任务中性能优于基线方法。
- 首次可严格证明同时应对两类异常点，适合高可靠性场景。
事件流是现实世界中重要的数据形式，通常遵循随时间变化的规律模式。然而，这些模式可能受到意外缺失或异常出现事件的干扰。本文采用时间点过程框架学习事件流，并提出一种简单但高效的方法，同时处理误增和遗漏事件异常点。具体而言，引入一种新颖的权重函数，动态调节每个观测事件的重要性，使最终估计器具备多重统计优势。我们在事件流可分组的分类问题中将该方法与基线方法进行对比，理论与数值结果均证实了新方法的有效性。据我们所知，这是首个能严格证明同时处理两类异常点的方法。
原文摘要 · Abstract (English)
Event stream is an important data format in real life. The events are usually expected to follow some regular patterns over time. However, the patterns could be contaminated by unexpected absences or occurrences of events. In this paper, we adopt the temporal point process framework for learning event stream and we provide a simple-but-effective method to deal with both commission and omission event outliers.In particular, we introduce a novel weight function to dynamically adjust the importance of each observed event so that the final estimator could offer multiple statistical merits. We compare the proposed method with the vanilla one in the classification problems, where event streams can be clustered into different groups. Both theoretical and numerical results confirm the effectiveness of our new approach. To our knowledge, our method is the first one to provably handle both commission and omission outliers simultaneously.
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