Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Area under the ROC curve, aka AUC, is a measure of choice for assessing the performance
of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that …

Numerous machine learning models can be formulated as a stochastic minimax optimization
problem, such as imbalanced data classification with AUC maximization. Develo** …

Abstract Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural
network by maximizing the AUC score of the model on a dataset. Most previous works of …

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they
suffer from imbalanced labels due to limited fraud compared to the overall userbase. This …

Recently there are a considerable amount of work devoted to the study of the algorithmic
stability and generalization for stochastic gradient descent (SGD). However, the existing …

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as
imbalanced learning and recommender systems. The vast majority of existing AUC …

Traditional machine learning follows a close-set assumption that the training and test set
share the same label space. While in many practical scenarios, it is inevitable that some test …

Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common metrics for
evaluating classification performance for imbalanced problems. Compared with AUROC …

We consider nonconvex-concave minimax optimization problems of the form $\min_ {\bf
x}\max_ {\bf y\in {\mathcal Y}} f ({\bf x},{\bf y}) $, where $ f $ is strongly-concave in $\bf y $ but …