no code implementations • 13 Mar 2024 • Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
Causal representation learning aims at identifying high-level causal variables from perceptual data.
1 code implementation • 7 Nov 2023 • Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities.
no code implementations • 7 Sep 2022 • Danru Xu, Erdun Gao, Wei Huang, Menghan Wang, Andy Song, Mingming Gong
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidential reasoning.