2 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
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…