5 papers
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Gábor Lugosi, Marcos Matabuena
This paper introduces a framework for uncertainty quantification in regression models defined on metric spaces. Using a proposed notion of homoscedasticity, we define a conformal p…
Gaussian mixture models in Hilbert spaces via kernel methods
Daniel López-Montero, Antonio Ãlvarez-López, Marcos Matabuena
Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally…
Random-Effects Algorithm for Random Objects in Metric Spaces
Marcos Matabuena, Mateo Cámara
Across many scientific disciplines, multiple observations are collected from the same experimental units, and in modern datasets these observations often arise as non-Euclidean ran…
Continuous-Time Learning of Probability Distributions: A Case Study in a Digital Trial of Young Children with Type 1 Diabetes
Antonio Ãlvarez-López, Marcos Matabuena
Understanding how biomarker distributions evolve over time is a central challenge in digital health and chronic disease monitoring. In diabetes, changes in the distribution of gluc…
Uncertainty quantification in metric spaces
Gábor Lugosi, Marcos Matabuena
This paper introduces a novel uncertainty quantification framework for regression models where the response takes values in a separable metric space, and the predictors are in a Eu…