Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach
with the inherent conditional and label shifts, in which we aim to align the distributions wrt …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to an unlabeled and unseen target domain, which is usually trained on data …

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information
learned from a labeled source domain to facilitate the implementation in an unlabeled …

Supervised fairness-aware machine learning under distribution shifts is an emerging field
that addresses the challenge of maintaining equitable and unbiased predictions when faced …

Domain generalization (DG) approaches typically use the hypothesis learned on source
domains for inference on the unseen target domain. However, such a hypothesis can be …

The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data
scalability issue, while the existing works usually focus on classifying independently discrete …

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts
between source and target domains, by applying a well-performed model in an unlabeled …

Abstract Machine learning models fail to perform well on real-world applications when 1) the
category distribution P (Y) of the training dataset suffers from long-tailed distribution and 2) …