Jerónimo Hernández-González

32 papers B 1Journal 20Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare. J. Heal. Informatics Res. Vien Ngoc Dang, Víctor M. Campello, Jerónimo Hernández-González, Karim Lekadir
2025	J	`jnl`	Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning. Cogn. Comput. Esmeralda Ruiz Pujadas, Covadonga M. Díaz-Caneja, Dejan Stevanovic, Marta Ferrer Quintero, Carlos Martín-Isla, Jerónimo Hernández-González, Angélica Atehortúa, Noussair Lazrak, Lotta Pries, Philippe Delespaul, Marina Camacho, Sinan Gülöksüz, Bart P. F. Rutten, Karim Lekadir
2024	—	`conf`	Mitigating Overdiagnosis Bias in CNN-Based Alzheimer's Disease Diagnosis for the Elderly. FAIMI/EPIMI@MICCAI Vien Ngoc Dang, Adrià Casamitjana, Jerónimo Hernández-González, Karim Lekadir, Alzheimer's Disease Neuroimaging Initiative
2024	—	`conf`	Understanding Early-Life Stress Impact on Mental-Physical Comorbidity Using Machine Learning. SIPAIM Vien Ngoc Dang, Jerónimo Hernández-González, Karim Lekadir
2023	—	`conf`	Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease. CLIP/FAIMI/EPIMI@MICCAI Vien Ngoc Dang, Adrià Casamitjana, Martijn P. A. Starmans, Carlos Martín-Isla, Jerónimo Hernández-González, Karim Lekadir, Alzheimer's Disease Neuroimaging Initiative
2023	J	`jnl`	Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artif. Intell. Rev. Anna Cascarano, Jordi Mur-Petit, Jerónimo Hernández-González, Marina Camacho, Nina de Toro Eadie, Polyxeni Gkontra, Marc Chadeau-Hyam, Jordi Vitrià, Karim Lekadir
2023	J	`jnl`	On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique. IEEE Trans. Learn. Technol. Jerónimo Hernández-González, Pedro Javier Herrera
2023	J	`jnl`	On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels. Knowl. Inf. Syst. Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez
2022	J	`jnl`	Fairness and bias correction in machine learning for depression prediction: results from four different study populations. CoRR Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir
2022	J	`jnl`	Machine Learning From Crowds Using Candidate Set-Based Labeling. IEEE Intell. Syst. Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez
2022	J	`jnl`	Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models. Comput. Biol. Medicine Jerónimo Hernández-González, Olga Valls, Adrián Torres-Martín, Jesús Cerquides
2022	J	`jnl`	On the relative value of weak information of supervision for learning generative models: An empirical study. Int. J. Approx. Reason. Jerónimo Hernández-González, Aritz Pérez
2021	—	`conf`	Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection. CCIA Adrián Torres-Martín, Jerónimo Hernández-González, Jesús Cerquides
2020	J	`jnl`	A Robust Solution to Variational Importance Sampling of Minimum Variance. Entropy Jerónimo Hernández-González, Jesús Cerquides
2019	J	`jnl`	A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators. IEEE Trans. Knowl. Data Eng. Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2019	J	`jnl`	A framework for evaluation in learning from label proportions. Prog. Artif. Intell. Jerónimo Hernández-González
2019	J	`jnl`	Aggregated outputs by linear models: An application on marine litter beaching prediction. Inf. Sci. Jerónimo Hernández-González, Iñaki Inza, Igor Granado, Oihane C. Basurko, Jose A. Fernandes, José Antonio Lozano
2019	—	`conf`	Variational Importance Sampling: Initial Findings. CCIA Jerónimo Hernández-González, Joan Capdevila, Jesús Cerquides
2018	—	`conf`	Crowd Learning with Candidate Labeling: An EM-Based Solution. CAEPIA Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez
2018	—	`conf`	Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve. CAEPIA Jerónimo Hernández-González
2018	J	`jnl`	Learning to classify software defects from crowds: A novel approach. Appl. Soft Comput. Jerónimo Hernández-González, Daniel Rodríguez, Iñaki Inza, Rachel Harrison, José Antonio Lozano
2018	J	`jnl`	Weak Labeling for Crowd Learning. CoRR Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez
2017	J	`jnl`	Learning from Proportions of Positive and Unlabeled Examples. Int. J. Intell. Syst. Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2017	—	`conf`	Merging knowledge bases in different languages. TextGraphs@ACL Jerónimo Hernández-González, Estevam R. Hruschka Jr., Tom M. Mitchell
2016	J	`jnl`	Weak supervision and other non-standard classification problems: A taxonomy. Pattern Recognit. Lett. Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2015	—	`conf`	A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions. CAEPIA Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2015	J	`jnl`	Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection. Int. J. Intell. Syst. Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2014	J	`jnl`	Similarity networks for classification: a case study in the Horse Colic problem. CoRR Lluís Belanche, Jerónimo Hernández-González
2013	J	`jnl`	Learning Bayesian network classifiers from label proportions. Pattern Recognit. Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2013	—	`conf`	Learning from Crowds in Multi-dimensional Classification Domains. CAEPIA Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano
2012	B	`conf`	Similarity networks for heterogeneous data. ESANN Lluís Belanche, Jerónimo Hernández-González
2011	—	`conf`	Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions. CAEPIA Jerónimo Hernández-González, Iñaki Inza