mlsanityData to 7 Oct 2026

Learning under Commission and Omission Event Outliers

arXiv 2501.13599 ↗First posted 23 Jan 2025stat.ML cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 23 Jan 2025.

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15 of 54 references.

References · 15

  1. 2016Variational Inference: A Review for Statisticianscited by 1,466
  2. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  3. 2017A Rewriting System for Convex Optimization Problemscited by 178
  4. 2017A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clusteringcited by 16
  5. 2023Meta Temporal Point Processescited by 16
  6. 2017SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Sparkcited by 14
  7. 2020Latent Network Structure Learning from High Dimensional Multivariate Point Processescited by 12
  8. 2017Recurrent Poisson Factorization for Temporal Recommendationcited by 8
  9. 2021Deep Structural Point Process for Learning Temporal Interaction Networkscited by 8
  10. 2020Group Network Hawkes Processcited by 7
  11. 2021Sequential change-point detection for mutually exciting point processes over networkscited by 7
  12. 2019Event Outlier Detection in Continuous Timecited by 6
3 more
  1. 2021Row-clustering of a Point Process-valued Matrixcited by 1
  2. 2024On Robust Clustering of Temporal Point Processcited by 1
  3. 2024Unmixing Noise from Hawkes Process to Model Learned Physiological Eventscited by 1

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