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 …

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …

To deal with the domain shift between training and test samples, current methods have
primarily focused on learning generalizable features during training and ignore the …

Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …

In many machine learning applications, it is important for the model to provide confidence
scores that accurately capture its prediction uncertainty. Although modern learning methods …

Recently, invariant risk minimization (IRM) was proposed as a promising solution to address
out-of-distribution (OOD) generalization. However, it is unclear when IRM should be …

Theoretically, domain adaptation is a well-researched problem. Further, this theory has been
well-used in practice. In particular, we note the bound on target error given by Ben-David et …

The generalization with respect to domain shifts, as they frequently appear in applications
such as autonomous driving, is one of the remaining big challenges for deep learning …

Multi-calibration is a powerful and evolving concept originating in the field of algorithmic
fairness. For a predictor $ f $ that estimates the outcome $ y $ given covariates $ x $, and for …

Learning representations that capture the underlying data generating process is a key
problem for data efficient and robust use of neural networks. One key property for robustness …