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Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with 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
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