As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …

Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years,
which adapts classifiers to an unlabeled target domain by exploiting a labeled source …

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues
on graphs involve both attributes and topological structures. Existing work on fair graph …

Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge
transfer from a labeled source domain to an unlabeled target domain. However, when the …

Owing to the remarkable capability of extracting effective graph embeddings, graph
convolutional network (GCN) and its variants have been successfully applied to a broad …

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source
domain to an unlabeled target domain where both domains share a common label space …

This paper targets at a new and challenging setting of knowledge transfer from multiple
source domains to a single target domain, where target data is few shot or even one shot …

Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a
labeled source domain and adapts a trained model to an unlabeled target domain. To …

Abstract Domain adaptation has become an attractive learning paradigm, as it can leverage
source domains with rich labels to deal with classification tasks in an unlabeled target …

Abstract Domain adaptation utilizes labeled source domains to solve classification problems
in the unlabeled target domain. Previous domain adaptation methods consider global …