In practical machine learning settings, the data on which a model must make
predictions often come from a different distribution than the data it was
trained on. Here, we investigate the problem of unsupervised multi-source
domain adaptation, where a model is trained on labelled data from multiple
source domains and must make predictions on a domain for whic