170 citations · 178 across the 4 of their papers we have counts for
5 papers
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
Tatsuya Yamada, Hiroshi Morioka, Yoshinobu Kawahara
Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an important challenge is to model…
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…
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process
Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen
The nonlinear vector autoregressive (NVAR) model provides an appealing framework to analyze multivariate time series obtained from a nonlinear dynamical system. However, the innova…
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen, Hiroshi Morioka
Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we firs…