mlsanityData to 13 Oct 2026

Weakly Supervised Classification in High Energy Physics

arXiv 1702.00414 ↗First posted 1 Feb 2017hep-ph physics.data-an stat.MLcited by 21

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Abstract (arXiv)

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics - quark versus gluon tagging - we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Key facts. Cited by 21 papers in the map (data through 13 Oct 2026). First posted on arXiv 1 Feb 2017.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 5; 2024: 5; 2025: 3.

21 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  3. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  4. 2022Learning from Label Proportions by Learning with Label Noisecited by 30
  5. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  6. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
  7. 2023Easy Learning from Label Proportionscited by 20
  8. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  9. 2023PAC Learning Linear Thresholds from Label Proportionscited by 10
  10. 2023LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportionscited by 5
  11. 2024Optimistic Rates for Learning from Label Proportionscited by 5
  12. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
9 more
  1. 2025Nearly Optimal Sample Complexity for Learning with Label Proportionscited by 4
  2. 2022The Tensor Data Platform: Towards an AI-centric Database Systemcited by 3
  3. 2022Learning from Label Proportions with Instance-wise Consistencycited by 2
  4. 2023Label Differential Privacy via Aggregationcited by 1
  5. 2025Optimal Learning from Label Proportions with General Loss Functionscited by 1
  6. 2024Hardness of Learning Boolean Functions from Label Proportionscited by 0
  7. 2024FRACTAL: Fine-Grained Scoring from Aggregate Text Labelscited by 0
  8. 2024Learning from Label Proportions and Covariate-shifted Instancescited by 0
  9. 2025Strengthening Anomaly Awarenesscited by 0