Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions. - Jerónimo Hernández-González; Iñaki Inza (CAEPIA, 2011)

Similarity networks for heterogeneous data. - Lluís Belanche; Jerónimo Hernández-González (ESANN, 2012)

Learning from Crowds in Multi-dimensional Classification Domains. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (CAEPIA, 2013)

Learning Bayesian network classifiers from label proportions. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (Pattern Recognit., 2013)

Similarity networks for classification: a case study in the Horse Colic problem. - Lluís Belanche; Jerónimo Hernández-González (CoRR, 2014)

A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (CAEPIA, 2015)

Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (Int. J. Intell. Syst., 2015)

Weak supervision and other non-standard classification problems: A taxonomy. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (Pattern Recognit. Lett., 2016)

Merging knowledge bases in different languages. - Jerónimo Hernández-González; Estevam R. Hruschka Jr.; Tom M. Mitchell (TextGraphs@ACL, 2017)

Learning from Proportions of Positive and Unlabeled Examples. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (Int. J. Intell. Syst., 2017)

Crowd Learning with Candidate Labeling: An EM-Based Solution. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (CAEPIA, 2018)

Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve. - Jerónimo Hernández-González (CAEPIA, 2018)

Learning to classify software defects from crowds: A novel approach. - Jerónimo Hernández-González; Daniel Rodríguez 001; Iñaki Inza; Rachel Harrison; José Antonio Lozano 001 (Appl. Soft Comput., 2018)

Weak Labeling for Crowd Learning. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (CoRR, 2018)

Variational Importance Sampling: Initial Findings. - Jerónimo Hernández-González; Joan Capdevila; Jesús Cerquides (CCIA, 2019)

Aggregated outputs by linear models: An application on marine litter beaching prediction. - Jerónimo Hernández-González; Iñaki Inza; Igor Granado; Oihane C. Basurko; Jose A. Fernandes; José Antonio Lozano 001 (Inf. Sci., 2019)

A framework for evaluation in learning from label proportions. - Jerónimo Hernández-González (Prog. Artif. Intell., 2019)

A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators. - Jerónimo Hernández-González; Iñaki Inza; José Antonio Lozano 001 (IEEE Trans. Knowl. Data Eng., 2019)

A Robust Solution to Variational Importance Sampling of Minimum Variance. - Jerónimo Hernández-González; Jesús Cerquides (Entropy, 2020)

Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection. - Adrián Torres-Martín; Jerónimo Hernández-González; Jesús Cerquides (CCIA, 2021)

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models. - Jerónimo Hernández-González; Olga Valls; Adrián Torres-Martín; Jesús Cerquides (Comput. Biol. Medicine, 2022)

Fairness and bias correction in machine learning for depression prediction: results from four different study populations. - Vien Ngoc Dang; Anna Cascarano; Rosa H. Mulder; Charlotte Cecil; Maria A. Zuluaga; Jerónimo Hernández-González; Karim Lekadir (CoRR, 2022)

Machine Learning From Crowds Using Candidate Set-Based Labeling. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (IEEE Intell. Syst., 2022)

On the relative value of weak information of supervision for learning generative models: An empirical study. - Jerónimo Hernández-González; Aritz Pérez (Int. J. Approx. Reason., 2022)

Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease. - Vien Ngoc Dang; Adrià Casamitjana; Martijn P. A. Starmans; Carlos Martín-Isla; Jerónimo Hernández-González; Karim Lekadir; Alzheimer's Disease Neuroimaging Initiative (CLIP/FAIMI/EPIMI@MICCAI, 2023)

On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels. - Iker Beñaran-Muñoz; Jerónimo Hernández-González; Aritz Pérez (Knowl. Inf. Syst., 2023)

Machine and deep learning for longitudinal biomedical data: a review of methods and applications. - Anna Cascarano; Jordi Mur-Petit; Jerónimo Hernández-González; Marina Camacho; Nina de Toro Eadie; Polyxeni Gkontra; Marc Chadeau-Hyam; Jordi Vitrià; Karim Lekadir (Artif. Intell. Rev., 2023)

On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique. - Jerónimo Hernández-González; Pedro Javier Herrera (IEEE Trans. Learn. Technol., 2023)

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer's Disease Diagnosis for the Elderly. - Vien Ngoc Dang; Adrià Casamitjana; Jerónimo Hernández-González; Karim Lekadir; Alzheimer's Disease Neuroimaging Initiative (FAIMI/EPIMI@MICCAI, 2024)

Understanding Early-Life Stress Impact on Mental-Physical Comorbidity Using Machine Learning. - Vien Ngoc Dang; Jerónimo Hernández-González; Karim Lekadir (SIPAIM, 2024)

Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare. - Vien Ngoc Dang; Víctor M. Campello; Jerónimo Hernández-González; Karim Lekadir (J. Heal. Informatics Res., 2025)