9 citations · 41 across the 15 of their papers we have counts for
9 papers · 1 filter
Optimal Transport Group Counterfactual Explanations
Enrique Valero-Leal, Bernd Bischl, Pedro Larrañaga +2
Group counterfactual explanations find a set of counterfactual instances to explain a group of input instances contrastively. However, existing methods either (i) optimize counterf…
Bandwidth Selectors on Semiparametric Bayesian Networks
Victor Alejandre, Concha Bielza, Pedro Larrañaga
Semiparametric Bayesian networks (SPBNs) integrate parametric and non-parametric probabilistic models, offering flexibility in learning complex data distributions from samples. In…
Binned semiparametric Bayesian networks for efficient kernel density estimation
Rafael Sojo, Javier Díaz-Rozo, Concha Bielza +1
This paper introduces a new type of probabilistic semiparametric model that takes advantage of data binning to reduce the computational cost of kernel density estimation in nonpara…
Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks
David Quesada, Pedro Larrañaga, Concha Bielza
When we face patients arriving to a hospital suffering from the effects of some illness, one of the main problems we can encounter is evaluating whether or not said patients are go…
Semiparametric Bayesian Networks
David Atienza, Concha Bielza, Pedro Larrañaga
We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both co…
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza
In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each sta…
Towards Gaussian Bayesian Network Fusion
Irene Córdoba, Concha Bielza, Pedro Larrañaga
Data sets are growing in complexity thanks to the increasing facilities we have nowadays to both generate and store data. This poses many challenges to machine learning that are le…
Markov Property in Generative Classifiers
Gherardo Varando, Concha Bielza, Pedro Larrañaga +1
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions. Discrimination functions of undirecte…
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato +2
The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is henc…