1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…