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。

迭代加深超带

Iterative Deepening Hyperband

Algorithm configuration (AC) is concerned with the automated search of the
most suitable parameter configuration of a parametrized algorithm. There is
currently a wide variety of AC problem variants and methods proposed in the
literature. Existing reviews do not take into account all derivatives of the AC
problem, nor do they offer a complete classification scheme. To this end, we
introduce taxonomies to describe the AC problem and features of configuration
methods, respectively. We review existing AC literature within the lens of our
taxonomies, outline relevant design choices of configuration approaches,
contrast methods and problem variants against each other, and describe the
state of AC in industry. Finally, our review provides researchers and
practitioners with a look at future research directions in the field of AC.

该论文介绍了关于算法配置问题的各种变体和方法，提出了用于描述算法配置问题和配置方法特征的分类法，并在此基础上回顾了已有的算法配置文献，提出了各种配置方法的设计选择，以及针对不同问题变体的对比方法，最终总结了行业中算法配置的现状并为未来研究提供了方向。

自动算法配置方法综述

A Survey of Methods for Automated Algorithm Configuration

Deep Learning has enabled remarkable progress over the last years on a
variety of tasks, such as image recognition, speech recognition, and machine
translation. One crucial aspect for this progress are novel neural
architectures. Currently employed architectures have mostly been developed
manually by human experts, which is a time-consuming and error-prone process.
Because of this, there is growing interest in automated neural architecture
search methods. We provide an overview of existing work in this field of
research and categorize them according to three dimensions: search space,
search strategy, and performance estimation strategy.

本文通过分类讨论搜索空间、搜索策略与性能估计策略三种维度，为神经架构自动搜索方法的存在研究提供了一个概述。