Uncertainty Quantification in Pairwise Difference Learning for Classification
arXiv:2610.21554v1 Announce Type: cross Abstract: Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. We propose a version of PDL for the task of classification and propose a suitab…