Learning under commission and omission event outliers
针对事件流中多报和漏报两种异常点，提出一种基于时间点过程的加权方法，能同时处理两类异常，在分类和变点检测任务中优于传统方法。
Abstract 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, including unbiasedness when there are no outliers and robustness when there exist outliers. The proposed method can be applied into various downstream tasks. We compare our method with the vanilla one in two specific downstream tasks, the classification problem and the change point detection problem. Both theoretical and numerical results confirm the effectiveness of our new approach. To our knowledge, the proposed method is the first one to provably handle both commission and omission outliers simultaneously.