Automated machine learning (AutoML) systems propose an end-to-end solution to
a given machine learning problem, creating either fixed or flexible pipelines.
Fixed pipelines are task independent constructs: their general composition
remains the same, regardless of the data. In contrast, the structure of
flexible pipelines varies depending on the input, making them finely tailored
to individual tasks. However, flexible pipelines can be structurally
overcomplicated and have poor explainability. We propose the EVOSA approach
that compensates for the negative points of flexible pipelines by incorporating
a sensitivity analysis which increases the robustness and interpretability of
the flexible solutions. EVOSA quantitatively estimates positive and negative
impact of an edge or a node on a pipeline graph, and feeds this information to
the evolutionary AutoML optimizer. The correctness and efficiency of EVOSA was
validated in tabular, multimodal and computer vision tasks, suggesting
generalizability of the proposed approach across domains.

自动化机器学习系统提出了一种针对给定机器学习问题的端到端解决方案，创建了固定或灵活的流程。 EVOSA 方法通过引入敏感性分析来解决灵活流程的负面因素，并提高了灵活解决方案的鲁棒性和可解释性。

演化自动机器学习与复合管道的结构敏感性分析的集成

Integration Of Evolutionary Automated Machine Learning With Structural  Sensitivity Analysis For Composite Pipelines

Automated Machine Learning (AutoML) is an area of research that focuses on
developing methods to generate machine learning models automatically. The idea
of being able to build machine learning models with very little human
intervention represents a great opportunity for the practice of applied machine
learning. However, there is very little information on how to design an AutoML
system in practice. Most of the research focuses on the problems facing
optimization algorithms and leaves out the details of how that would be done in
practice. In this paper, we propose a frame of reference for building general
AutoML systems. Through a narrative review of the main approaches in the area,
our main idea is to distill the fundamental concepts in order to support them
in a single design. Finally, we discuss some open problems related to the
application of AutoML for future research.

自动化机器学习（AutoML）是一个研究领域，专注于开发自动生成机器学习模型的方法。本文提出了一个建立通用 AutoML 系统的参考框架，并通过对该领域主要方法的叙述性回顾，提炼了基本概念以支持其设计，并讨论了 AutoML 应用中的一些未解决问题，以供未来研究参考。