6 citations · 27 across the 17 of their papers we have counts for
4 papers · 1 filter
Amortized Equation Discovery in Hybrid Dynamical Systems
Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas +1
Hybrid dynamical systems are prevalent in science and engineering to express complex systems with continuous and discrete states. To learn the laws of systems, all previous methods…
Learning Causal Abstractions of Linear Structural Causal Models
Riccardo Massidda, Sara Magliacane, Davide Bacciu
The need for modelling causal knowledge at different levels of granularity arises in several settings. Causal Abstraction provides a framework for formalizing this problem by relat…
Towards the Reusability and Compositionality of Causal Representations
Davide Talon, Phillip Lippe, Stuart James +2
Causal Representation Learning (CRL) aims at identifying high-level causal factors and their relationships from high-dimensional observations, e.g., images. While most CRL works fo…
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…