Uncertainty, Risk and Related Concepts in Machine Learning

机器学习是一门理解数据的科学。它没有也不可能消除所有的风险和不确定性。这个项目建议研究机器学习如何使用、表示和交流风险和不确定性的基础。它的目的是通过在相关学科中出现的不同概念之间找到新的理论联系来做到这一点。这些因素包括风险、不确定性、评分规则和损失函数、分歧、统计数据以及聚合信息的不同方式。通过建立更完整的理论地图，预计将开发新的机器学习方法，但更重要的是，机器学习将能够更好地整合到更大的社会技术系统中。

Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.