Jerónimo Hernández-González

Pedro Javier Herrera

IEEE Trans. Learn. Technol., December, 2023

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

[BibT_eX]

[DOI]

Anna Cascarano

Jordi Mur-Petit

Artif. Intell. Rev., November, 2023

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

[BibT_eX]

[DOI]

Knowl. Inf. Syst., January, 2023

Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease.

[BibT_eX]

[DOI]

Vien Ngoc Dang

Adrià Casamitjana

Martijn P. A. Starmans

Carlos Martín-Isla

Alzheimer's Disease Neuroimaging Initiative

Karim Lekadir

Proceedings of the Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 2023

2022

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

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2022

Machine Learning From Crowds Using Candidate Set-Based Labeling.

[BibT_eX]

[DOI]

IEEE Intell. Syst., 2022

Fairness and bias correction in machine learning for depression prediction: results from four different study populations.

[BibT_eX]

[DOI]

Karim Lekadir

CoRR, 2022

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

[BibT_eX]

[DOI]

Olga Valls

Adrián Torres-Martín

Comput. Biol. Medicine, 2022

2021

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

[BibT_eX]

[DOI]

Adrián Torres-Martín

Proceedings of the Artificial Intelligence Research and Development, 2021

2020

A Robust Solution to Variational Importance Sampling of Minimum Variance.

[BibT_eX]

[DOI]

Entropy, 2020

2019

A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2019

A framework for evaluation in learning from label proportions.

[BibT_eX]

[DOI]

Prog. Artif. Intell., 2019

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

[BibT_eX]

[DOI]

Inf. Sci., 2019

Variational Importance Sampling: Initial Findings.

[BibT_eX]

[DOI]

Joan Capdevila

Proceedings of the Artificial Intelligence Research and Development, 2019

2018

Weak Labeling for Crowd Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Learning to classify software defects from crowds: A novel approach.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2018

Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2018

Crowd Learning with Candidate Labeling: An EM-Based Solution.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2018

2017

Learning from Proportions of Positive and Unlabeled Examples.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2017

Merging knowledge bases in different languages.

[BibT_eX]

[DOI]

Estevam R. Hruschka Jr.

Tom M. Mitchell

Proceedings of TextGraphs@ACL 2017: the 11th Workshop on Graph-based Methods for Natural Language Processing, 2017

2016

Weak supervision and other non-standard classification problems: A taxonomy.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2016

2015

Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2015

A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2015

2014

Similarity networks for classification: a case study in the Horse Colic problem.

[BibT_eX]

[DOI]

Lluís Belanche

CoRR, 2014

2013

Learning Bayesian network classifiers from label proportions.

[BibT_eX]

[DOI]

Pattern Recognit., 2013

Learning from Crowds in Multi-dimensional Classification Domains.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2013

2012

Similarity networks for heterogeneous data.

[BibT_eX]

[DOI]

Lluís Belanche

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions.

[BibT_eX]

[DOI]