3 papers
stat.ML2026
Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
Kane Warrior, Dalia Chakrabarty
In probabilstic supervised learning of an input-output relationship - as a sample function of a Gaussian Process (GP) - priors are typically specified for the hyperparameters of th…
stat.ML2026
Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
Kane Warrior, Dalia Chakrabarty
Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Standard Gaussian process kernels w…
stat.ML2025
Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning
Gargi Roy, Dalia Chakrabarty
We introduce parametrisation of that property of the available training dataset, that necessitates an inhomogeneous correlation structure for the function that is learnt as a model…