2 citations · 2 across the 5 of their papers we have counts for
5 papers
MORE-PLR: multi-output regression employed for partial label ranking
Santo M. A. R. Thies, Juan C. Alfaro, Viktor Bengs
The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input…
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 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…
A comparative analysis of rank aggregation methods for the partial label ranking problem
Jiayi Wang, Juan C. Alfaro, Viktor Bengs
The label ranking problem is a supervised learning scenario in which the learner predicts a total order of the class labels for a given input instance. Recently, research has incre…