9 citations · 41 across the 15 of their papers we have counts for
19 papers
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…
Actionable Counterfactual Explanations Using Bayesian Networks and Path Planning with Applications to Environmental Quality Improvement
Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza
Counterfactual explanations study what should have changed in order to get an alternative result, enabling end-users to understand machine learning mechanisms with counterexamples.…
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…
Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search
Vicente P. Soloviev, Vedran Dunjko, Concha Bielza +2
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the probl…
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…
Context-specific kernel-based hidden Markov model for time series analysis
Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga +1
Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidde…
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga
Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide…
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…
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…
A community-based transcriptomics classification and nomenclature of neocortical cell types
Rafael Yuste, Michael Hawrylycz, Nadia Aalling +68
To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological…
On generating random Gaussian graphical models
Irene Córdoba, Gherardo Varando, Concha Bielza +1
Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (i…
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…
A fast Metropolis-Hastings method for generating random correlation matrices
Irene Córdoba, Gherardo Varando, Concha Bielza +1
We propose a novel Metropolis-Hastings algorithm to sample uniformly from the space of correlation matrices. Existing methods in the literature are based on elaborated representati…
A partial orthogonalization method for simulating covariance and concentration graph matrices
Irene Córdoba, Gherardo Varando, Concha Bielza +1
Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (i…
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…
A review of Gaussian Markov models for conditional independence
Irene Córdoba, Concha Bielza, Pedro Larrañaga
Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in direc…