Learning under Commission and Omission Event Outliers

• URL: http://arxiv.org/abs/2501.13599v1
• Date: Thu, 23 Jan 2025 12:08:21 GMT
• Title: Learning under Commission and Omission Event Outliers
• Authors: Yuecheng Zhang, Guanhua Fang, Wen Yu,
• Abstract summary: Event stream is an important data format in real life. In this paper, we adopt the temporal point process framework for learning event stream. We provide a simple-but-effective method to deal with both commission and omission event outliers.
• Score: 10.4442505961159
• License:
• 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. 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.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Event Detection in Time Series: Universal Deep Learning Approach [0.0]
  Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. We propose a novel supervised regression-based deep learning approach that offers several advantages over classification-based methods. Our approach can effectively handle various types of events within a unified framework, including rare events and imbalanced datasets.
  arXiv  Detail & Related papers  (2023-11-27T09:33:56Z)
• Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain [9.71405768795797]
  Temporal Point Processes play an important role in predicting or forecasting events. We propose a scalable and efficient approach based on TPPs to solve this problem.
  arXiv  Detail & Related papers  (2023-09-29T06:46:31Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes [25.074394338483575]
  A large fraction of data generated via human activities can be represented as a sequence of events over a continuous-time. Deep learning models over these continuous-time event sequences is a non-trivial task. In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
  arXiv  Detail & Related papers  (2022-06-23T18:23:20Z)
• Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach [0.7476901945542385]
  This paper presents a complete framework for extracting and processing crude oil-related events found in CrudeOilNews corpus. We place special emphasis on event properties (Polarity, Modality, and Intensity) classification to determine the factual certainty of each event.
  arXiv  Detail & Related papers  (2022-05-01T03:21:18Z)
• Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention [56.46649994444616]
  We present a novel approach to semi-supervised new event type induction using a masked contrastive loss. We extend our approach to two new tasks: predicting the type name of the discovered clusters and linking them to FrameNet frames.
  arXiv  Detail & Related papers  (2022-02-12T00:32:22Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Bad and good errors: value-weighted skill scores in deep ensemble learning [0.0]
  We introduce a strategy for assessing the severity of forecast errors based on the evidence. We introduce a novel definition of confusion matrix and skill scores giving greater importance to the value of the prediction. We show the performances of this approach in the case of three applications concerned with pollution, space weather and stock prize forecasting.
  arXiv  Detail & Related papers  (2021-03-04T08:05:13Z)
• Extensively Matching for Few-shot Learning Event Detection [66.31312496170139]
  Event detection models under super-vised learning settings fail to transfer to new event types. Few-shot learning has not beenexplored in event detection. We propose two novelloss factors that matching examples in the sup-port set to provide more training signals to themodel.
  arXiv  Detail & Related papers  (2020-06-17T18:30:30Z)
• Exploiting the Matching Information in the Support Set for Few Shot Event Classification [66.31312496170139]
  We investigate event classification under the few-shot learningsetting. We propose a novel training method for this problem that exten-sively exploit the support set during the training process. Our experiments ontwo benchmark EC datasets show that the proposed method can improvethe best reported few-shot learning models by up to 10% on accuracy for event classification.
  arXiv  Detail & Related papers  (2020-02-13T00:40:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.