# Constraining the Parameters of High-Dimensional Models with Active Learning

arXiv: https://arxiv.org/abs/1905.08628 · HTML: https://mlsanity.com/paper/1905.08628/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2019.

## Built on by (most cited first, 8 of 8)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models](https://mlsanity.com/paper/2501.05382/) (2025)
- [Exploration of Parameter Spaces Assisted by Machine Learning](https://mlsanity.com/paper/2207.09959/) (2022)
- [Active Learning in Physics: From 101, to Progress, and Perspective](https://mlsanity.com/paper/2307.03899/) (2023)
- [Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations](https://mlsanity.com/paper/2203.16683/) (2022)
- [DLScanner: A parameter space scanner package assisted by deep learning methods](https://mlsanity.com/paper/2412.19675/) (2024)
- [Active Learning with Variational Quantum Circuits for Quantum Process Tomography](https://mlsanity.com/paper/2412.20925/) (2024)
- [Improving Oil Slick Trajectory Simulations with Bayesian Optimization](https://mlsanity.com/paper/2503.02749/) (2025)

## Abstract (arXiv)

Constraining the parameters of physical models with >5-10 parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub.