A short bio

I am post-doc researcher at the Artificial Intelligence Research Institute. In the field of machine learning, my research works have been related to learning problems with weak supervision. Our proposals aim to learn predictive models, specifically Bayesian network classifiers, from this kind of data.

I did my PhD studies at the Intelligent Systems Group of the University of the Basque Country under the supervision of Iñaki Inza and Jose A. Lozano.

Publications

  • Apr2019

    A framework for evaluation in learning from label proportions

    J. Hernández-González

    In Progress in Artificial Intelligence in press, 2019 · Additional data 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, 2019

  • 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, 2019 · Additional data 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), 2018 · Additional data external link

  • 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, 2018 · Additional data 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, 2018

  • 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, 2018 · Additional data 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, 2017

  • 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, 2017

  • 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), 2016

  • 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 · Additional data 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, 2016

  • 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), 2015

  • 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, 2015

  • Dec2013

    Learning Bayesian network classifiers from label proportions

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

    Pattern Recognition 46(12): 3425-3440, 2013 · Additional data 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), 2013

  • 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), 2011

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)