7 papers
Anchor PCA
Benedikt Seiter, Anya Fries, Julius von Kügelgen +1
Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal…
From Pixels to Components: Eigenvector Masking for Visual Representation Learning
Alice Bizeul, Thomas Sutter, Alain Ryser +3
Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches o…
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
Jack Brady, Julius von Kügelgen, Sébastien Lachapelle +3
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 o…
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood, Julius von Kügelgen, Linus Ericsson +4
Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data. However, with downstream tasks generally unknown…
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf +1
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights. Whereas the classical interventional interp…
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
Julius von Kügelgen
Causal models provide rich descriptions of complex systems as sets of mechanisms by which each variable is influenced by its direct causes. They support reasoning about manipulatin…