  Modern machine learning models are often constructed taking into account
multiple objectives, e.g., minimizing inference time while also maximizing
accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return
such candidate models, and the approximation of the Pareto front is used to
assess their performance. In practice, we also want to measure generalization
when moving from the validation to the test set. However, some of the models
might no longer be Pareto-optimal which makes it unclear how to quantify the
performance of the MHPO method when evaluated on the test set. To resolve this,
we provide a novel evaluation protocol that allows measuring the generalization
performance of MHPO methods and studying its capabilities for comparing two
optimization experiments.
