244 citations · 964 across the 44 of their papers we have counts for
45 papers
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
Ambroise Heurtebise, Omar Chehab, Pierre Ablin +2
Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications pro…
A noise-corrected Langevin algorithm and sampling by half-denoising
Aapo Hyvärinen
The Langevin algorithm is a classic method for sampling from a given pdf in a real space. In its basic version, it only requires knowledge of the gradient of the log-density, also…
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
Hermanni Hälvä, Jonathan So, Richard E. Turner +1
Recently, nonlinear ICA has surfaced as a popular alternative to the many heuristic models used in deep representation learning and disentanglement. An advantage of nonlinear ICA i…
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
Hiroshi Morioka, Aapo Hyvärinen
A topic of great current interest is Causal Representation Learning (CRL), whose goal is to learn a causal model for hidden features in a data-driven manner. Unfortunately, CRL is…
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
Aapo Hyvarinen, Ilyes Khemakhem, Hiroshi Morioka
A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called "disentanglement". Most approaches are heuristic an…
Identifiability of latent-variable and structural-equation models: from linear to nonlinear
Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Monti
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i.e. some parameters cannot be uniquely estimated. In factor (component) analysis…