244 citations · 964 across the 44 of their papers we have counts for
7 papers · 1 filter
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…
Uncovering the structure of clinical EEG signals with self-supervised learning
Hubert Banville, Omar Chehab, Aapo Hyvärinen +2
Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data,…
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…
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Hermanni Hälvä, Aapo Hyvärinen
Recent advances in nonlinear Independent Component Analysis (ICA) provide a principled framework for unsupervised feature learning and disentanglement. The central idea in such wor…
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele, Giancarlo Fissore, Adrián Javaloy +2
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning. A popular approach for solving it is mapping the observations in…
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…