Therefore, estimating how well a given model might perform on the new data is an important step toward reliable ML applications. This isverychallenging, however,asthedata distribution can change inflexible ways, and we may not haveanylabels on the new data, which is often the case in monitoring settings. In this paper, we propose a new distribution shift model, Sparse Joint Shift (SJS), which considers the joint shift of both labels and afew features.

Figure 10: Visual results on Pascal validation set with 60 labels.

Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using otherapproaches intermsofaccuracy.