# Learning under Commission and Omission Event Outliers

arXiv: https://arxiv.org/abs/2501.13599 · HTML: https://mlsanity.com/paper/2501.13599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Meta Temporal Point Processes](https://mlsanity.com/paper/2301.12023/) (2023)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark](https://mlsanity.com/paper/1709.08920/) (2017)
- [Latent Network Structure Learning from High Dimensional Multivariate Point Processes](https://mlsanity.com/paper/2004.03569/) (2020)
- [Recurrent Poisson Factorization for Temporal Recommendation](https://mlsanity.com/paper/1703.01442/) (2017)
- [Group Network Hawkes Process](https://mlsanity.com/paper/2002.08521/) (2020)
- [Deep Structural Point Process for Learning Temporal Interaction Networks](https://mlsanity.com/paper/2107.03573/) (2021)
- [Sequential change-point detection for mutually exciting point processes over networks](https://mlsanity.com/paper/2102.05724/) (2021)
- [Event Outlier Detection in Continuous Time](https://mlsanity.com/paper/1912.09522/) (2019)
- [Row-clustering of a Point Process-valued Matrix](https://mlsanity.com/paper/2110.01207/) (2021)
- [On Robust Clustering of Temporal Point Process](https://mlsanity.com/paper/2405.17828/) (2024)
- [Unmixing Noise from Hawkes Process to Model Learned Physiological Events](https://mlsanity.com/paper/2406.16938/) (2024)

## Abstract (arXiv)

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.