Architecture-agnostic approaches rely solely on the feature-label pairs (x, y) present in the tasks (and not some auxiliary information like the weights of the optimal model). For example, one of the most compelling architecture-agnostic approaches, the optimal transport distance for datasets (OTDD), does not require training of the network on multiple tasks (or datasets). It has a theoretical guarantee and explanation since it originates from optimal transport theory and can also be applied if the labels between two datasets are disjoint. However, the computational cost of this method is high, limiting its applicability. To increase computational efficiency, entropy regularization has been introduced, which creates a tradeoff between computational cost and accuracy.