The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups.

Machine learning is now an integral part of decision-making processes across various domains, e.g., medical applications [

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Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.

This pruning algorithm then assigns an importance scorekdRdwlwlk to each weight, and remove the weights receiving the lowest such scores. In Figure 8, we plot the generalization of the family of models each aforementioned algorithm generates as a function of sparsities and training time in epochs. In Section 1, We show that the augmented training algorithm produces VGG-16 models withgeneralization thatisindistinguishable fromthatofmodels thatpruning withlearning rate rewinding produces. We refer to the topK% of training examples whose training loss improves the most during pruning as thetop-improved examples. To examine the influence of these top-improved examples ongeneralization, for each sparsity pruning reaches, we train twodense models ontwo datasets respectively: a). the original training dataset excluding the top-improved examples at the specifiedsparsity,whichwedenoteasTIE(Top-ImprovedExamples);b).