Hyperparameter optimization (HPO) is concerned with the automated search for
the most appropriate hyperparameter configuration (HPC) of a parameterized
machine learning algorithm. A state-of-the-art HPO method is Hyperband, which,
however, has its own parameters that influence its performance. One of these
parameters, the maximal budget, is especially problematic: If chosen too small,
the budget needs to be increased in hindsight and, as Hyperband is not
incremental by design, the entire algorithm must be re-run. This is not only
costly but also comes with a loss of valuable knowledge already accumulated. In
this paper, we propose incremental variants of Hyperband that eliminate these
drawbacks, and show that these variants satisfy theoretical guarantees
qualitatively similar to those for the original Hyperband with the "right"
budget. Moreover, we demonstrate their practical utility in experiments with
benchmark data sets.

本文提出了超频参数 Hyperband 的增量变体，以消除选择过小的大预算所引起的昂贵成本和有价值已积累知识的流失问题，并在基准数据集实验中展示了它们的实用性和理论保证的类似性。