72 citations · 310 across the 41 of their papers we have counts for
16 papers · 1 filter
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…
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…
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sébastien Lachapelle +4
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 high-d…
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…
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao, Danru Xu, Sébastien Lachapelle +5
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partial…
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu +3
To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding…
Provably Learning Object-Centric Representations
Jack Brady, Roland S. Zimmermann, Yash Sharma +3
Learning structured representations of the visual world in terms of objects promises to significantly improve the generalization abilities of current machine learning models. While…
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen +4
In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics fo…
Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll +4
Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of i…
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf
Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated…
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello +4
Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involv…
Backward-Compatible Prediction Updates: A Probabilistic Approach
Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner +3
When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the i…
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Lukas Schott, Julius von Kügelgen, Frederik Träuble +6
An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the wo…
On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt +3
Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individua…
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf +1
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships betwe…