Different distribution shifts require different algorithmic and operatio...
To ensure the out-of-distribution (OOD) generalization performance,
trad...
Domain generalization aims to solve the challenge of Out-of-Distribution...
Massive amounts of data are the foundation of data-driven recommendation...
As an intrinsic and fundamental property of big data, data heterogeneity...
The invariance property across environments is at the heart of invariant...
Despite the striking performance achieved by modern detectors when train...
The ability to generalize under distributional shifts is essential to
re...
Classic machine learning methods are built on the i.i.d. assumption that...
Machine learning algorithms with empirical risk minimization are vulnera...
Machine learning algorithms with empirical risk minimization usually suf...
Machine learning algorithms with empirical risk minimization are vulnera...
Generative adversarial networks (GANs) have shown promise in image gener...