Most statistical learning algorithms rely on an over-simplified assumption, that is, the train
and test data are independent and identically distributed. In real-world scenarios, however, it …

Federated Learning (FL) offers a powerful strategy for training machine learning models
across decentralized datasets while maintaining data privacy, yet domain shifts among …

Abstract Domain generalization aims to accurately predict unknown data using models
trained by known domain data. Learning domain-invariant representations based on causal …

Deep learning models are usually black boxes when deployed on machine learning
platforms. Prior works have shown that the attributes (eg, the number of convolutional layers) …

Deep models often struggle with out-of-distribution (OOD) generalization, limiting their real-
world applicability beyond controlled laboratory settings. Invariant risk minimization (IRM) …

Domain-invariant representation learning is a powerful method for domain generalization.
Previous approaches face challenges such as high computational demands, training …

Medical data often exhibits distribution shifts, which cause test-time performance
degradation for deep learning models trained using standard supervised learning pipelines …

Traditional domain generalization methods often rely on domain alignment to reduce inter-
domain distribution differences and learn domain-invariant representations. However …