It is widely recognized and intuitive that knowledge-sharing methods work best when the source and target tasks are similar (i.e., more relevant). Under some circumstances, knowledge-sharing methods may result in inferior models. For example, transfer learning may decrease performance on the target task (a phenomenon known as "negative transfer" ), e.g., if the source and target data distribution are too different. In multi-task learning, it is well known that naively adding source tasks can result in lower performance, and choosing a subset of similar tasks can mitigate the negative transfer problem. In the case of meta-learning, the performance improvement depends, among other factors, on the number of available training tasks and their relevance to the target task. Several methods have been proposed for quantifying the relatedness (i.e., distance or similarity) between tasks. For example, the discrepancy distance quantifies the distance between source and target tasks. The measure relies on the loss function and hypothesis space of the predictive function. The utility of the discrepancy distance for establishing generalization bounds in domain adaptation (a sub-field of transfer learning) depends on various parameters, including the complexity of the hypothesis class (often quantified using VC-dimension and Rademacher complexity). These complexities limit the applicability of the discrepancy distance in real-world scenarios.