5 citations · 19 across the 17 of their papers we have counts for
4 papers · 2 filters
Learning DAGs without imposing acyclicity
Gherardo Varando
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we…
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
Irene Córdoba, Concha Bielza, Pedro Larrañaga +1
The sparse Cholesky parametrization of the inverse covariance matrix can be interpreted as a Gaussian Bayesian network; however its counterpart, the covariance Cholesky factor, has…
Graphical continuous Lyapunov models
Gherardo Varando, Niels Richard Hansen
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process. This parametrization can be interpreted as a new graphic…
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen +3
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information…