Machine learning techniques have become pervasive through many technical fields but an obstacle for employment is often the criterion of robustness. While machine learning can be a great means to improve upon the quality of traditional optimization techniques in uncritical scenarios (e.g., a customized online search result that proposes to a consumer a more or less well-fitting new product advertisement), it may be prohibitive to employ when directly embedded in critical decision flows (e.g., a self-driving car that needs to decide whether to engage an emergency brake). In the latter case, robustness is one mandatory constraint. Robustness can have many facets; some of them are covered by this timely special issue that represents the state of the art from a design and test point of view. Many thanks to the Guest Editors Theocharis Theocharides, Muhammad Shafique, Jungwook Choi, and Onur Mutlu for editing this special issue that includes two keynote articles, a survey on “Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead,” and six technical articles.

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来自EIC：强大的机器学习

机器学习技术已经在许多技术领域中普及，但就业的障碍通常是稳健性的标准。虽然机器学习是在非关键情况下（例如，向消费者建议或多或少适合新产品广告的定制在线搜索结果）可以提高传统优化技术质量的一种很好的手段，但它可能会禁止直接嵌入关键决策流程时使用（例如，需要决定是否使用紧急制动器的自动驾驶汽车）。在后一种情况下，鲁棒性是一个强制性约束。健壮性可以有很多方面。从设计和测试的角度来看，其中一些代表了最先进的技术，这本及时的特刊涵盖了其中。非常感谢客座编辑Theocharis Theocharides，Muhammad Shafique，