About me

I am a lecturer and researcher at the Department of Computer Science, Applied Mathematics and Statistics of the University of Girona. I am interested in machine learning problems in which data uncertainty/missingness is a determining characteristic, such as weakly supervised learning. I also study probabilistic graphical models for probabilistic inference and machine learning. Many of my works aim to learn probabilistic graphical models, commonly Bayesian networks, from weakly labeled data. I have applied these developments to problems from different domains, such as biomedical, environmental, and educational domains.

Publications

  • Dec2023

    On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique

    J. Hernández-González, P.J. Herrera

    In IEEE Transactions on Learning Technologies 16(6): 926-939 · Supplementary material external link

  • Dec2023

    Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The POSTCARDS Study

    J. Villar, J.M. González-Martín, J. Hernández-González, et al.

    In Critical Care Medicine 51(12): 1638-1649

  • Aug2023

    Machine and deep learning for longitudinal biomedical data: a review of methods and applications

    A. Cascarano, J. Mur-Petit, J. Hernández-González, M. Camacho, N.T. Eadie, P. Gkontra, M. Chadeau-Hyam, J. Vitria, K. Lekadir

    In Artificial Intelligence Review 56: 1711-1771

  • Jan2023

    On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels

    I. Beñaran-Muñoz, J. Hernández-González, A. Pérez

    In Knowledge and Information Systems 65: 241-260

  • Oct2022

    Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

    J. Hernández-González, O. Valls, A. Torres-Martín, J. Cerquides

    In Computers in Biology and Medicine 150: 106160 · Supplementary material external link

  • Sep2022

    Machine learning from crowds using candidate set-based labelling

    I. Beñaran-Muñoz, J. Hernández-González, A. Pérez

    In IEEE Intelligent Systems 37(6): 57-68

  • Aug2022

    On the relative value of weak information of supervision for learning generative models: An empirical study

    J. Hernández-González, A. Pérez

    In International Journal of Approximate Reasoning 150: 258-272 · Supplementary material external link

  • Oct2021

    Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection

    A. Torres-Martín, J. Hernández-González, J. Cerquides

    In Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence (CCIA)

  • May2021

    A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

    J. Cerquides, M.O. Mulayim, J. Hernández-González, A.R. Shankar, J.L. Fernández-Márquez

    In Mathematics 9(8): 875

  • Dec2020

    A Robust Solution to Variational Importance Sampling of Minimum Variance

    J. Hernández-González, J. Cerquides

    In Entropy 22(12): 1405

  • Oct2019

    Variational importance sampling: initial findings

    J. Hernández-González, J. Capdevila, J. Cerquides

    In Proceedings of the 22nd International Conference of the Catalan Association for Artificial Intelligence (CCIA)

  • Apr2019

    A framework for evaluation in learning from label proportions

    J. Hernández-González

    In Progress in Artificial Intelligence 8 (3): 359-373 · Supplementary material external link

  • Apr2019

    Beach litter forecasting on the SE coast of the Bay of Biscay: A bayesian networks approach

    I. Granado, O.C. Basurko, A. Rubio, L. Ferrer, J. Hernández-González, I. Epelde, J.A. Fernandes

    In Continental Shelf Research 180: 14-23

  • Jan2019

    Aggregated outputs by linear models: An application on marine litter beaching prediction

    J. Hernández-González, I. Inza, I. Granado, O.C. Basurko, J.A. Fernandes, J.A. Lozano

    In Information Sciences 481: 381-393 · Supplementary material external link

  • Oct2018

    Evaluation in learning from label proportions: an approximation to the precision-recall curve

    J. Hernández-González

    In Proceedings of the 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA) · Supplementary material external link

  • Oct2018

    Crowd learning with candidate labeling: an EM-based solution

    I. Beñaran-Muñoz, J. Hernández-González, A. Párez

    In Proceedings of the 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)

  • Jun2018

    A note on the behavior of majority voting in multi-class domains with biased annotators

    J. Hernández-González, I. Inza, J.A. Lozano

    In IEEE Transactions on Knowledge and Data Engineering 31(1): 195-200 · Supplementary material external link

  • Jan2018

    Two datasets of defect reports labeled by a crowd of annotators of unknown reliability

    J. Hernández-González, D. Rodriguez, I. Inza, R. Harrison, J.A. Lozano

    In Data in Brief 18: 840-845

  • Jan2018

    Learning to classify software defects from crowds: A novel approach

    J. Hernández-González, D. Rodriguez, I. Inza, R. Harrison, J.A. Lozano

    In Applied Soft Computing Journal 62: 579-591 · Supplementary material external link

  • Aug2017

    Merging knowledge bases in different languages

    J. Hernández-González, Estevam R. Hruschka Jr., Tom M. Mitchell

    In Proceedings of the 11th TextGraphs Workshop at ACL'17

  • Feb2017

    Learning from proportions of positive and unlabeled examples

    J. Hernández-González, I. Inza, J.A. Lozano

    International Journal of Intelligent Systems 32: 109--133

  • Sep2016

    Whatever you know, just tell me something: Crowd learning with free supervision

    J. Hernández-González, I. Inza, J.A. Lozano

    In Proceedings of the VIII Symposium of Data Mining Theory and Applications (TAMIDA)

  • May2016

    Fitting the data from embryo implantation prediction: learning from label proportions

    J. Hernández-González, I. Inza, L. Crisol-Ortiz, M.A. Guembe, M.J. Iñarra, J.A. Lozano

    Statistical Methods in Medical Research 27(4): 1056-1066, 2018 · Supplementary material external link

  • Jan2016

    Weak supervision and other non-standard classification problems: a taxonomy

    J. Hernández-González, I. Inza, J.A. Lozano

    Pattern Recognition Letters 69: 49-55

  • Nov2015

    A novel weakly supervised problem: Learning from positive-unlabeled proportions

    J. Hernández-González, I. Inza, J.A. Lozano

    In Proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)

  • Jan2015

    Multidimensional learning from crowds: usefulness and application of expertise detection

    J. Hernández-González, I. Inza, J.A. Lozano

    International Journal of Intelligent Systems 30(3): 326-354

  • Dec2013

    Learning Bayesian network classifiers from label proportions

    J. Hernández-González, I. Inza, J.A. Lozano

    Pattern Recognition 46(12): 3425-3440 · Supplementary material external link

  • Sep2013

    Learning from crowds in multi-dimensional classification domains

    J. Hernández-González, I. Inza, J.A. Lozano

    In Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)

  • Nov2011

    Learning naive Bayes models for Multiple-Instance Learning with label proportions

    J. Hernández-González, I. Inza

    In Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)

Special mentions

  • 2018

    Best PhD thesis award

    University of the Basque Country

  • 2016

    Best idea award

    Open Data Euskadi

  • 2015

    Best student paper award

    16th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)