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

Emergence and causality are two fundamental concepts for understanding complex
systems. They are interconnected. On one hand, emergence refers to the phenomenon …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Graph representation learning has shown effectiveness when testing and training graph
data come from the same distribution, but most existing approaches fail to generalize under …

The invariance principle from causality is at the heart of notable approaches such as
invariant risk minimization (IRM) that seek to address out-of-distribution (OOD) …

Distributional shift is one of the major obstacles when transferring machine learning
prediction systems from the lab to the real world. To tackle this problem, we assume that …

Abstract Machine learning algorithms typically assume that training and test examples are
drawn from the same distribution. However, distribution shift is a common problem in real …

Despite recent success in using the invariance principle for out-of-distribution (OOD)
generalization on Euclidean data (eg, images), studies on graph data are still limited …

Machine learning systems typically assume that the distributions of training and test sets
match closely. However, a critical requirement of such systems in the real world is their …

Abstract Machine learning algorithms with empirical risk minimization usually suffer from
poor generalization performance due to the greedy exploitation of correlations among the …