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 …

The large gap between the generalization level of state-of-the-art machine learning and
human learning systems calls for the development of artificial intelligence (AI) models that …

This work introduces a novel principle for disentanglement we call mechanism sparsity
regularization, which applies when the latent factors of interest depend sparsely on …

Molecules are essential building blocks of life and their different conformations (ie, shapes)
crucially determine the functional role that they play in living organisms. Cryogenic Electron …

This paper addresses intervention-based causal representation learning (CRL) under a
general nonparametric latent causal model and an unknown transformation that maps the …

Causal representation learning aims at identifying high-level causal variables from
perceptual data. Most methods assume that all latent causal variables are captured in the …

Causal disentanglement aims to learn about latent causal factors behind data, holding the
promise to augment existing representation learning methods in terms of interpretability and …

Learning disentangled representations of concepts and re-composing them in unseen ways
is crucial for generalizing to out-of-domain situations. However, the underlying properties of …

Modern applications often leverage multiple views of a subject of study. Within
neuroscience, there is growing interest in large-scale simultaneous recordings across …

Multimodal contrastive representation learning methods have proven successful across a
range of domains, partly due to their ability to generate meaningful shared representations …