45 citations · 56 across the 16 of their papers we have counts for
3 papers · 1 filter
Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Marcos Matabuena, Xiaokai Luo, Oscar Hernan Madrid Padilla
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. Howe…
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and ra…
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
Marcos Matabuena, Paulo Félix, Carlos Meijide-Garcia +1
AEGIS study possesses unique information on longitudinal changes in circulating glucose through continuous glucose monitoring technology (CGM). However, as usual in longitudinal me…