4 citations · 8 across the 11 of their papers we have counts for
11 papers
Federated Learning of AnDE Classifiers
Pablo Torrijos, Juan C. Alfaro, José A. Gámez +1
This work presents a federated framework for training Averaged -Dependence Estimators (AnDE) in distributed environments. The proposed method focuses on the discriminative setti…
Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms
Pablo Torrijos, José A. Gámez, José M. Puerta
This paper introduces an evolutionary computation approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited treewidth. The consensus BN aims…
Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding
Óscar Alcarria, Rafael Sánchez, Javier Sempere +5
Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We…
Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
Pablo Torrijos, José A. Gámez, José M. Puerta +1
Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency preservation with computational tractability. While unrestricted fusion…
Federated Causal Discovery via Regression-Directed Cumulants
Pablo Torrijos, Fabio Stella, José A. Gámez +1
In this paper we study linear non-Gaussian acyclic models (LiNGAM) when used in federated environments. These causal models allow one to go beyond Markov equivalence. However, in m…
Bayesian Network Structural Consensus via Greedy Min-Cut Analysis
Pablo Torrijos, José M. Puerta, Juan A. Aledo +1
This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated le…
FedGES: A Federated Learning Approach for BN Structure Learning
Pablo Torrijos, José A. Gámez, José M. Puerta
Bayesian Network (BN) structure learning traditionally centralizes data, raising privacy concerns when data is distributed across multiple entities. This research introduces Federa…
Federated Learning with Discriminative Naive Bayes Classifier
Pablo Torrijos, Juan C. Alfaro, José A. Gámez +1
Federated Learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. This paper proposes a new feder…
Enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search
Jorge D. Laborda, Pablo Torrijos, José M. Puerta +1
This article presents MCTS-BN, an adaptation of the Monte Carlo Tree Search (MCTS) algorithm for the structural learning of Bayesian Networks (BNs). Initially designed for game tre…
A Ring-Based Distributed Algorithm for Learning High-Dimensional Bayesian Networks
Jorge D. Laborda, Pablo Torrijos, José M. Puerta +1
Learning Bayesian Networks (BNs) from high-dimensional data is a complex and time-consuming task. Although there are approaches based on horizontal (instances) or vertical (variabl…
MiniAnDE: a reduced AnDE ensemble to deal with microarray data
Pablo Torrijos, José A. Gámez, José M. Puerta
This article focuses on the supervised classification of datasets with a large number of variables and a small number of instances. This is the case, for example, for microarray da…