This approachyields biased estimates ofthe expected risk, and therefore leads to poor decisions for two reasons.

Through a deeper understanding of predictions of neural networks, Influence Function (IF) has been applied to various tasks such as detecting and relabeling mislabeled samples, dataset pruning, and separation of data sources in practice.

Zhang et al. have recently shown that geodesic convex concave Riemannian problems always admit saddle-point solutions.

While the toy and scRNA-seq datasets do not have a split, we used the training and test set of CIFAR-10 jointly for the unsupervised UMAP dimensionreduction.