  Learning from Label Proportions (LLP) is an established machine learning
problem with numerous real-world applications. In this setting, data items are
grouped into bags, and the goal is to learn individual item labels, knowing
only the features of the data and the proportions of labels in each bag.
Although LLP is a well-established problem, it has several unusual aspects that
create challenges for benchmarking learning methods. Fundamental complications
arise because of the existence of different LLP variants, i.e., dependence
structures that can exist between items, labels, and bags. Accordingly, the
first algorithmic challenge is the generation of variant-specific datasets
capturing the diversity of dependence structures and bag characteristics. The
second methodological challenge is model selection, i.e., hyperparameter
tuning; due to the nature of LLP, model selection cannot easily use the
standard machine learning paradigm. The final benchmarking challenge consists
of properly evaluating LLP solution methods across various LLP variants. We
note that there is very little consideration of these issues in prior work, and
there are no general solutions for these challenges proposed to date. To
address these challenges, we develop methods capable of generating LLP datasets
meeting the requirements of different variants. We use these methods to
generate a collection of datasets encompassing the spectrum of LLP problem
characteristics, which can be used in future evaluation studies. Additionally,
we develop guidelines for benchmarking LLP algorithms, including the model
selection and evaluation steps. Finally, we illustrate the new methods and
guidelines by performing an extensive benchmark of a set of well-known LLP
algorithms. We show that choosing the best algorithm depends critically on the
LLP variant and model selection method, demonstrating the need for our proposed
approach.
