  We introduce ordered transfer hyperparameter optimisation (OTHPO), a version
of transfer learning for hyperparameter optimisation (HPO) where the tasks
follow a sequential order. Unlike for state-of-the-art transfer HPO, the
assumption is that each task is most correlated to those immediately before it.
This matches many deployed settings, where hyperparameters are retuned as more
data is collected; for instance tuning a sequence of movie recommendation
systems as more movies and ratings are added. We propose a formal definition,
outline the differences to related problems and propose a basic OTHPO method
that outperforms state-of-the-art transfer HPO. We empirically show the
importance of taking order into account using ten benchmarks. The benchmarks
are in the setting of gradually accumulating data, and span XGBoost, random
forest, approximate k-nearest neighbor, elastic net, support vector machines
and a separate real-world motivated optimisation problem. We open source the
benchmarks to foster future research on ordered transfer HPO.
