In the past, MI datasets were frequently proprietary, confined to particular institutions, and stored in private repositories. In this particular setting, there is a pressing need for alternative models of data sharing, documentation, and governance. Within this context,theemergence ofCommunityContributed Platforms (CCPs) presented a potential for the public sharing of medical datasets.

In this paper, we study learning-augmented algorithms for the Bahncard problem.

Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms.