mlsanityData to 14 Oct 2026

Decorrelation with conditional normalizing flows

arXiv 2211.02486 ↗First posted 4 Nov 2022hep-ph cs.LGcited by 1

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Abstract (arXiv)

The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we show that a normalizing flow conditioned on the protected attributes can be used to find a decorrelated representation for any discriminant. As a normalizing flow is invertible the separation power of the resulting discriminant will be unchanged at any fixed value of the protected attributes. We demonstrate the efficacy of our approach by building supervised jet taggers that produce almost no sculpting in the mass distribution of the background.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 4 Nov 2022.

Builds on

8 of 35 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  3. 2019Neural Spline Flowscited by 596
  4. 2020Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimizationcited by 90
  5. 2016Learning to Pivot with Adversarial Networkscited by 55
  6. 2015Jet-Images -- Deep Learning Editioncited by 41
  7. 2017Decorrelated Jet Substructure Tagging using Adversarial Neural Networkscited by 12
  8. 2022Null Hypothesis Test for Anomaly Detectioncited by 7

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Citing papers per year: 2023: 1.

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