To ensure the out-of-distribution (OOD) generalization performance,
trad...
The problem of covariate-shift generalization has attracted intensive
re...
The invariance property across environments is at the heart of invariant...
Despite the remarkable performance that modern deep neural networks have...
Personalized pricing is a business strategy to charge different prices t...
The ability to generalize under distributional shifts is essential to
re...
Classic machine learning methods are built on the i.i.d. assumption that...
Domain generalization (DG) aims to help models trained on a set of sourc...
Machine learning algorithms with empirical risk minimization are vulnera...
Machine learning algorithms with empirical risk minimization usually suf...
Approaches based on deep neural networks have achieved striking performa...
Nowadays fairness issues have raised great concerns in decision-making
s...
Machine learning algorithms with empirical risk minimization are vulnera...
The I.I.D. hypothesis between training data and testing data is the basi...
Image classification is one of the fundamental problems in computer visi...