62 citations · 125 across the 26 of their papers we have counts for
6 papers · 1 filter
Linear Independent Component Analysis via Optimal Transport
Ashutosh Jha, Michel Besserve, Simon Buchholz
Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize no…
Controlling for discrete unmeasured confounding in nonlinear causal models
Patrick Burauel, Frederick Eberhardt, Michel Besserve
Unmeasured confounding is a major challenge for identifying causal relationships from non-experimental data. Here, we propose a method that can accommodate unmeasured discrete conf…
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
Hamza Keurti, Hsiao-Ru Pan, Michel Besserve +2
How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations c…
Learning soft interventions in complex equilibrium systems
Michel Besserve, Bernhard Schölkopf
Complex systems often contain feedback loops that can be described as cyclic causal models. Intervening in such systems may lead to counterintuitive effects, which cannot be inferr…
Exploring the Latent Space of Autoencoders with Interventional Assays
Felix Leeb, Stefan Bauer, Michel Besserve +1
Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods. However, without…
Structure by Architecture: Structured Representations without Regularization
Felix Leeb, Guilia Lanzillotta, Yashas Annadani +3
We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling. Unlike most methods which rely on ma…