5 papers
Transfer learning for nonparametric Bayesian networks
Rafael Sojo, Pedro Larrañaga, Concha Bielza
This paper introduces two transfer learning methodologies for estimating nonparametric Bayesian networks under scarce data. We propose two algorithms, a constraint-based structure…
Binned semiparametric Bayesian networks for efficient kernel density estimation
Rafael Sojo, Javier DÃaz-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…
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…