  Despite the recent progress in hyperparameter optimization (HPO), available
benchmarks that resemble real-world scenarios consist of a few and very large
problem instances that are expensive to solve. This blocks researchers and
practitioners not only from systematically running large-scale comparisons that
are needed to draw statistically significant results but also from reproducing
experiments that were conducted before. This work proposes a method to
alleviate these issues by means of a meta-surrogate model for HPO tasks trained
on off-line generated data. The model combines a probabilistic encoder with a
multi-task model such that it can generate inexpensive and realistic tasks of
the class of problems of interest. We demonstrate that benchmarking HPO methods
on samples of the generative model allows us to draw more coherent and
statistically significant conclusions that can be reached orders of magnitude
faster than using the original tasks. We provide evidence of our findings for
various HPO methods on a wide class of problems.
