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 …

Nonconvex minimax problems appear frequently in emerging machine learning
applications, such as generative adversarial networks and adversarial learning. Simple …

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

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

In the paper, we propose a class of efficient mirror descent ascent methods to solve the
nonsmooth nonconvex-strongly-concave minimax problems by using dynamic mirror …

Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA)
and Extragradient (EG) methods for the convex-concave minimax problems, little is known …

This paper considers stochastic first-order algorithms for minimax optimization under Polyak-
{\L} ojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem …

In this paper, we present a novel method for the automatic classification of medical images
that learns and leverages weak causal signals in the image. Our framework consists of a …

X-risk is a term introduced to represent a family of compositional measures or objectives, in
which each data point is compared with a large number of items explicitly or implicitly for …