mlsanityData to 7 Oct 2026

The Landscape of Unfolding with Machine Learning

arXiv 2404.18807 ↗First posted 29 Apr 2024hep-ph cs.LG hep-excited by 20

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Abstract (arXiv)

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 26 of its 86 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

26 of 86 references.

References · 26

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14 more
  1. 2019Cubic-Spline Flowscited by 34
  2. 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
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  6. 2020Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencecited by 21
  7. 2021Generative Networks for Precision Enthusiastscited by 21
  8. 2022An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative trainingcited by 20
  9. 2021Understanding Event-Generation Networks via Uncertaintiescited by 17
  10. 2023Improving Generative Model-based Unfolding with Schr"odinger Bridgescited by 13
  11. 2022nu-Flows: Conditional Neutrino Regressioncited by 11
  12. 2021Scaffolding Simulations with Deep Learning for High-dimensional Deconvolutioncited by 9
  13. 2024Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusioncited by 8
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 9.

20 papers.

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  1. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
  2. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 27
  3. 2024Generative Unfolding with Distribution Mappingcited by 6
  4. 2026Scaling laws for amplitude surrogatescited by 5
  5. 2025Simulation-Prior Independent Neural Unfolding Procedurecited by 4
  6. 2026Uncertainty in Physics and AI: Taxonomy, Quantification, and Validationcited by 3
  7. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  8. 2026Generative Models and Statistical Validationcited by 2
  9. 2024Multidimensional Deconvolution with Profilingcited by 1
  10. 2025Discriminative versus Generative Approaches to Simulation-based Inferencecited by 1
  11. 2026Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problemscited by 1
  12. 2026An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learningcited by 1
8 more
  1. 2026Unknown Unknowns: Model Misspecification in Machine Learning for Physicscited by 1
  2. 2024Enhancing generalization in high energy physics using white-box adversarial attackscited by 0
  3. 2025Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breakingcited by 0
  4. 2025Generator Based Inference (GBI)cited by 0
  5. 2025Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameterscited by 0
  6. 2026Unfolding with a Wasserstein Losscited by 0
  7. 2026ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physicscited by 0
  8. 2026Machine learning for the LHC physics program: a 2025-2026 stocktakecited by 0