4 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…