Gherardo Varando

Orcid: 0000-0002-6708-1103

According to our database1, Gherardo Varando authored at least 30 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Structural learning of simple staged trees.
Data Min. Knowl. Discov., May, 2024

Pairwise causal discovery with support measure machines.
Appl. Soft Comput., January, 2024

Large Language Models for Constrained-Based Causal Discovery.
CoRR, 2024

Learning Staged Trees from Incomplete Data.
CoRR, 2024

Context-Specific Refinements of Bayesian Network Classifiers.
CoRR, 2024

Recovering Latent Confounders from High-dimensional Proxy Variables.
CoRR, 2024

Improving generalisation via anchor multivariate analysis.
CoRR, 2024

Double machine learning for causal hybrid modeling - applications in the Earth sciences.
CoRR, 2024

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear.
Appl. Intell., 2024

2023
Learning latent functions for causal discovery.
Mach. Learn. Sci. Technol., September, 2023

A new class of generative classifiers based on staged tree models.
Knowl. Based Syst., 2023

Discovering Causal Relations and Equations from Data.
CoRR, 2023

Context-Specific Causal Discovery for Categorical Data Using Staged Trees.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
The <i>R</i> Package stagedtrees for Structural Learning of Stratified Staged Trees.
J. Stat. Softw., 2022

Highly Efficient Structural Learning of Sparse Staged Trees.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021
Staged trees and asymmetry-labeled DAGs.
CoRR, 2021

2020
On generating random Gaussian graphical models.
Int. J. Approx. Reason., 2020

Learning DAGs without imposing acyclicity.
CoRR, 2020

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data.
IEEE Access, 2020

Graphical continuous Lyapunov models.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values.
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019

2018
Theoretical studies on Bayesian network classifiers.
PhD thesis, 2018

Markov Property in Generative Classifiers.
CoRR, 2018

A partial orthogonalization method for simulating covariance and concentration graph matrices.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018

2016
Decision functions for chain classifiers based on Bayesian networks for multi-label classification.
Int. J. Approx. Reason., 2016

2015
A survey on multi-output regression.
WIREs Data Mining Knowl. Discov., 2015

Decision boundary for discrete Bayesian network classifiers.
J. Mach. Learn. Res., 2015

Conditional Density Approximations with Mixtures of Polynomials.
Int. J. Intell. Syst., 2015

2014
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification.
Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014


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