121 citations · 154 across the 6 of their papers we have counts for
6 papers
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…
Causal Autoregressive Flows
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech +1
Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an…
Autoregressive flow-based causal discovery and inference
Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvarinen
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual…
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma +1
We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional ene…
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti +1
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the d…