A bstract Hadronization is a critical step in the simulation of high-energy particle and nuclear
physics experiments. As there is no first principles understanding of this process, physically …

Abstract Machine learning tools have empowered a qualitatively new way to perform
differential cross section measurements whereby the data are unbinned, possibly in many …

The performance of machine learning algorithms depends to a large extent on the amount
and the quality of data available for training. Simulations are most often used as test-beds for …

A common setting for scientific inference is the ability to sample from a high-fidelity forward
model (simulation) without having an explicit probability density of the data. We propose a …

Most measurements in particle and nuclear physics use matrix-based unfolding algorithms
to correct for detector effects. In nearly all cases, the observable is defined analogously at …

Quantification, ie, the task of training predictors of the class prevalence values in sets of
unlabelled data items, has received increased attention in recent years. However, most …

There is a growing use of neural network classifiers as unbinned, high-dimensional (and
variable-dimensional) reweighting functions. To date, the focus has been on marginal …

Quantification, ie, the task of predicting the class prevalence values in bags of unlabeled
data items, has received increased attention in recent years. However, most quantification …

Machine Learning under Resource Constraints addresses novel machine learning
algorithms that are challenged by high-throughput data, by high dimensions, or by complex …

Many analyses in particle and nuclear physics use simulations to infer fundamental,
effective, or phenomenological parameters of the underlying physics models. When the …