论文标题
对机器学习模型的强大培训和认证的概述和预期前景
An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models
论文作者
论文摘要
在本讨论文件中，我们调查了有关机器学习模型鲁棒性的最新研究。随着学习算法在数据驱动的控制系统中越来越流行，必须确保它们对数据不确定性的鲁棒性，以维持可靠的安全至关重要的操作。我们首先回顾了这种鲁棒性的常见形式主义，然后继续讨论训练健壮机器学习模型的流行和最新技术，以及可证明这种鲁棒性的方法。从强大的机器学习的统一中，我们识别并讨论了该地区未来研究的急救方向。
In this discussion paper, we survey recent research surrounding robustness of machine learning models. As learning algorithms become increasingly more popular in data-driven control systems, their robustness to data uncertainty must be ensured in order to maintain reliable safety-critical operations. We begin by reviewing common formalisms for such robustness, and then move on to discuss popular and state-of-the-art techniques for training robust machine learning models as well as methods for provably certifying such robustness. From this unification of robust machine learning, we identify and discuss pressing directions for future research in the area.
