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stat.ML2026
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…
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…