1 code implementation • 11 Jun 2024 • Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls
We frame conditional independence queries as prompts to LLMs and employ the PC algorithm with the answers.
no code implementations • 28 May 2024 • Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices.
no code implementations • 28 May 2024 • Manuele Leonelli, Gherardo Varando
Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy.
1 code implementation • 21 Mar 2024 • Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation.
no code implementations • 4 Mar 2024 • Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls
We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation.
no code implementations • 20 Feb 2024 • Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.
no code implementations • 21 May 2023 • Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
no code implementations • 2 Jan 2023 • Manuele Leonelli, Gherardo Varando
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables.
no code implementations • 14 Jun 2022 • Manuele Leonelli, Gherardo Varando
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined.
no code implementations • 8 Mar 2022 • Manuele Leonelli, Gherardo Varando
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector.
no code implementations • 4 Aug 2021 • Gherardo Varando, Federico Carli, Manuele Leonelli
Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph.
no code implementations • 8 Jun 2021 • Manuele Leonelli, Gherardo Varando
Causal discovery algorithms aim at untangling complex causal relationships from data.
no code implementations • 26 Dec 2020 • Federico Carli, Manuele Leonelli, Gherardo Varando
Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule.
2 code implementations • 4 Jun 2020 • Gherardo Varando
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint.
no code implementations • 2 Jun 2020 • Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
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 received, with few notable exceptions, little attention so far, despite having a natural interpretation as a hidden variable model for ordered signal data.
2 code implementations • 21 May 2020 • Gherardo Varando, Niels Richard Hansen
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process.
1 code implementation • 14 Apr 2020 • Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data.
1 code implementation • 21 Feb 2020 • Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
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 Processing Systems 2019 (NeurIPS).
no code implementations • 12 Nov 2018 • Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions.