A central problem in unsupervised deep learning is how to find useful representations of
high-dimensional data, sometimes called" disentanglement." Most approaches are heuristic …

An old problem in multivariate statistics is that linear Gaussian models are often
unidentifiable. In factor analysis, an orthogonal rotation of the factors is unidentifiable, while …

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 …

We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …

Abstract Independent Component Analysis (ICA) aims to recover independent latent
variables from observed mixtures thereof. Causal Representation Learning (CRL) aims …

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

Learning structured representations of the visual world in terms of objects promises to
significantly improve the generalization abilities of current machine learning models. While …

Independent component analysis provides a principled framework for unsupervised
representation learning, with solid theory on the identifiability of the latent code that …

In disentangled representation learning, a model is asked to tease apart a dataset's
underlying sources of variation and represent them independently of one another. Since the …

We tackle the problems of latent variables identification and" out-of-support''image
generation in representation learning. We show that both are possible for a class of …