Marcos Matabuena

Orcid: 0000-0003-3841-4447

According to our database1, Marcos Matabuena authored at least 14 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2025
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces.
CoRR, July, 2025

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces.
CoRR, June, 2025

Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data.
CoRR, May, 2025

Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age.
CoRR, January, 2025

Functional time transformation model with applications to digital health.
Comput. Stat. Data Anal., 2025

2024
Neural interval-censored survival regression with feature selection.
Stat. Anal. Data Min., August, 2024

Conformal Prediction in Dynamic Biological Systems.
CoRR, 2024

kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection.
CoRR, 2024

2022
Neural interval-censored Cox regression with feature selection.
CoRR, 2022

Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations.
Comput. Methods Programs Biomed., 2022

2021
COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques.
Comput. Methods Programs Biomed., 2021

2020
Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology.
CoRR, 2020

2019
The FA Quantifier Fuzzification Mechanism: analysis of convergence and efficient implementations.
CoRR, 2019

Application of Functional Data Analysis for the Prediction of Maximum Heart Rate.
IEEE Access, 2019


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