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 …

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 …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

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

The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …

Conditional stochastic optimization has found applications in a wide range of machine
learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the …

Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax
optimization, is widely used in practical applications such as generative adversarial …

In this paper, we consider nonconvex minimax optimization, which is gaining prominence in
many modern machine learning applications, such as GANs. Large-scale edge-based …

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

This paper studies the algorithmic stability and generalizability of decentralized stochastic
gradient descent (D-SGD). We prove that the consensus model learned by D-SGD is …