Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Abstract Domain generalization (DG) is essentially an out-of-distribution problem, aiming to
generalize the knowledge learned from multiple source domains to an unseen target …

Self-supervised representation learning has shown remarkable success in a number of
domains. A common practice is to perform data augmentation via hand-crafted …

Learning high-level causal representations together with a causal model from unstructured
low-level data such as pixels is impossible from observational data alone. We prove under …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

Intelligent agents should be able to learn useful representations by observing changes in
their environment. We model such observations as pairs of non-iid images sharing at least …

We construct an unsupervised learning model that achieves nonlinear disentanglement of
underlying factors of variation in naturalistic videos. Previous work suggests that …

Conventional supervised learning methods, especially deep ones, are found to be sensitive
to out-of-distribution (OOD) examples, largely because the learned representation mixes the …

Abstract Neuro-Symbolic (NeSy) predictive models hold the promise of improved
compliance with given constraints, systematic generalization, and interpretability, as they …