Identifiability of latent-variable and structural-equation models: from linear to nonlinear

Nonlinear independent component analysis for discrete-time and continuous-time signals

(2023) Alexander Schell et al.   ANNALS OF STATISTICS

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

(2023) Yongjie Zhu et al.   NEUROIMAGE

Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning

(2023) Aapo Hyvärinen et al.   Patterns

Identifiability of Gaussian structural equation models with equal error variances

(2013) J. Peters et al.   BIOMETRIKA

ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders

(2013) Tatsuya Tashiro et al.   NEURAL COMPUTATION

Investigating the electrophysiological basis of resting state networks using magnetoencephalography

(2011) M. J. Brookes et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

(2009) Aapo Hyvärinen et al.   NEUROIMAGE

Estimation of causal effects using linear non-Gaussian causal models with hidden variables

(2008) Patrik O. Hoyer et al.   INTERNATIONAL JOURNAL OF APPROXIMATE REASONING