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 …

Most of the existing chest X-ray datasets include labels from a list of findings without
specifying their locations on the radiographs. This limits the development of machine …

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 …

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 …

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 …

This paper studies the complexity for finding approximate stationary points of nonconvex-
strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth …

To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …

In this paper, we propose systematic and efficient gradient-based methods for both one-way
and two-way partial AUC (pAUC) maximization that are applicable to deep learning. We …