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 …

This article considers predicting future statuses of multiple agents in an online fashion by
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of
people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from …

Kernel-based methods exhibit well-documented performance in various nonlinear learning
tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task …

Multi-source domain adaptation is a challenging topic in transfer learning, especially when
the data of each domain are represented by different kinds of features, ie, Multi-source …

Learning to improve AUC performance for imbalanced data is an important machine
learning research problem. Most methods of AUC maximization assume that the model …

Kernel-basedlearning has well-documented merits in various machine learning tasks. Most
of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which …

Deep AUC maximization (DAM) is a popular method to deal with complex imbalanced
classification problems. It learns a deep neural network by minimizing a surrogate AUC loss …

Area under the receiver operating characteristics curve (AUC) is an important metric for a
wide range of machine-learning problems, and scalable methods for optimizing AUC have …