5 papers
Learning Random Geometric Graphs Drawn in Probabilistic Metric Spaces
Dalia Chakrabarty, Kangrui Wang, Chuqiao Zhang +1
We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning wor…
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…
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…
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…
A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures
Gargi Roy, Dalia Chakrabarty
We present a new strategy for learning the functional relation between a pair of variables, while addressing inhomogeneities in the correlation structure of the available data, by…