Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain
generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers …

Deep neural networks are able to memorize noisy labels easily with a softmax cross entropy
(CE) loss. Previous studies attempted to address this issue focus on incorporating a noise …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Transfer learning on graphs drawn from varied distributions (domains) is in great demand
across many applications. Emerging methods attempt to learn domain-invariant …

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …