mlsanityData to 7 Oct 2026

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics

arXiv 2107.02821 ↗First posted 6 Jul 2021stat.ML cs.LG hep-excited by 6

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Abstract (arXiv)

The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications. However, it has received relatively little attention in the broader ML community, as compared to point anomalies or other types of single instance outliers. One reason for this is the lack of powerful benchmark datasets. In this paper, we first explain how, after the Nobel-prize winning discovery of the Higgs boson, unsupervised group anomaly detection has become a new frontier of fundamental physics (where the motivation is to find new particles and forces). Then we propose a realistic synthetic benchmark dataset (LHCO2020) for the development of group anomaly detection algorithms. Finally, we compare several existing statistically-sound techniques for unsupervised group anomaly detection, and demonstrate their performance on the LHCO2020 dataset.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 6 Jul 2021.

Builds on

15 of 43 references.

References · 15

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  3. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  5. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  6. 2020Anomaly Detection with Density Estimationcited by 71
  7. 2013Classification with Asymmetric Label Noise: Consistency and Maximal Denoisingcited by 67
  8. 2020Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Featurescited by 64
  9. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 42
  10. 2020Perfect density models cannot guarantee anomaly detectioncited by 39
  11. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
  12. 2018Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativescited by 18
3 more
  1. 2013One-Class Support Measure Machines for Group Anomaly Detectioncited by 12
  2. 2014GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstractcited by 11
  3. 2018Group Anomaly Detection using Deep Generative Modelscited by 9

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 1.

6 papers.

  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  2. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  3. 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 7
  4. 2022Resonant Anomaly Detection with Multiple Reference Datasetscited by 4
  5. 2023Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimationcited by 0
  6. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0