Machine learning algorithms for automated decision making under domain shift

近年来，由于新算法的发展、数据的可用性、开源软件和不断增加的计算资源，机器学习（ML）取得了重大进展。然而，到目前为止，机器学习成功的大多数应用都局限于数据丰富且收集成本低的情况，例如图像、广告、网站用户交互和金融数据。相比之下，在许多现实世界的科学应用中，数据是稀缺的，收集和标记的成本很高，这就要求使用历史数据。不幸的是，这样的数据通常不能代表我们想要进行预测的新数据点，这个问题更正式地称为域移位。当用历史数据训练的模型与自主决策代理一起使用时，这种情况甚至会加剧。在这个项目中，你将努力解决机器学习的这些局限性。目标是开发改进的算法，能够更好地从数据中提取相关信息，而不是虚假的相关性，对领域转移具有鲁棒性，并且可以更好地推广到新情况，因此可以在决策代理中使用。为了评估方法的性能，您可以从重要的化学问题（如药物和材料发现）中处理现实世界的例子。

Machine Learning (ML) has made significant progress in recent years, powered by the development of new algorithms, availability of data, open-source software, and ever-increasing computational resources. However, most applications in which machine learning has been successful so far are limited to the cases where data is abundant and cheap to gather, such as images, advertising, website user interactions, and financial data. In contrast, in many real-world scientific applications, data is scarce and expensive to collect and label, which mandates the use of historic data. Unfortunately, such data is often not representative of novel data points on which we want to perform predictions, a problem more formally called domain shift. This is even exacerbated when models trained on historic data are used in conjunction with autonomous decision-making agents.In this project, you will work towards addressing these limitations of machine learning. The goal is to develop improved algorithms which are able to better extract relevant information from data instead of spurious correlations, are robust to domain shift and generalise better to novel situations, and can therefore be employed within decision making agents. To assess how the methods perform, you can address real-world examples from important chemistry problems, such as drug and materials discovery.