Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Modeling multivariate time series (MTS) is critical in modern intelligent systems. The
accurate forecast of MTS data is still challenging due to the complicated latent variable …

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer
knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset …

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning
capabilities. Recently, personalized FL (pFL) has received attention for its ability to address …

Contemporary domain generalization (DG) and multi-source unsupervised domain
adaptation (UDA) methods mostly collect data from multiple domains together for joint …

Abstract Domain generalization is the task of learning models that generalize to unseen
target domains. We propose a simple yet effective method for domain generalization, named …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …