4aa13186c795a52ba88f5b822f4b77eb-Paper-Conference.pdf

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.