570 citations · 3k across the 41 of their papers we have counts for
8 papers · 1 filter
Deconfounding Imitation Learning with Variational Inference
Risto Vuorio, Pim de Haan, Johann Brehmer +3
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidde…
A PAC-Bayesian Generalization Bound for Equivariant Networks
Arash Behboodi, Gabriele Cesa, Taco Cohen
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model. In this paper, we study how equivariance relate…
Towards a Grounded Theory of Causation for Embodied AI
Taco Cohen
There exist well-developed frameworks for causal modelling, but these require rather a lot of human domain expertise to define causal variables and perform interventions. In order…
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has sh…
Equivariant Mesh Attention Networks
Sourya Basu, Jose Gallego-Posada, Francesco Viganò +2
Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symme…
Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under…