# Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review

arXiv: https://arxiv.org/abs/2007.09121 · HTML: https://mlsanity.com/paper/2007.09121/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Bias and Priors in Machine Learning Calibrations for High Energy Physics](https://mlsanity.com/paper/2205.05084/) (2022)
- [Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks](https://mlsanity.com/paper/2501.05656/) (2025)
- [FAIR Universe HiggsML Uncertainty Dataset and Competition](https://mlsanity.com/paper/2410.02867/) (2024)
- [Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference](https://mlsanity.com/paper/2402.05330/) (2024)
- [Unknown Unknowns: Model Misspecification in Machine Learning for Physics](https://mlsanity.com/paper/2608.13633/) (2026)
- [Bridging Machine Learning and Sciences: Opportunities and Challenges](https://mlsanity.com/paper/2210.13441/) (2022)

## Abstract (arXiv)

In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search for optimal selection criteria and variable transformations. The introduction of nuisance parameters complicates the supervised learning task and its correspondence with the data analysis goal, due to their contribution degrading the model performances in real data, and the necessary addition of uncertainties in the resulting statistical inference. The approaches discussed include nuisance-parameterized models, modified or adversary losses, semi-supervised learning approaches, and inference-aware techniques.