69 citations · 324 across the 26 of their papers we have counts for
28 papers
Soft Random Graphs in Probabilistic Metric Spaces & Inter-graph Distance
Kangrui Wang, Dalia Chakrabarty
We present a new method for learning Soft Random Geometric Graphs (SRGGs), drawn in probabilistic metric spaces, with the connection function of the graph defined as the marginal p…
Learning in the Absence of Training Data -- a Galactic Application
Cedric Spire, Dalia Chakrabarty
There are multiple real-world problems in which training data is unavailable, and still, the ambition is to learn values of the system parameters, at which test data on an observab…
Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate & Scalar-Variate Gaussian Processes
Kangrui Wang, Dalia Chakrabarty
We undertake Bayesian learning of the high-dimensional functional relationship between a system parameter vector and an observable, that is in general tensor-valued. The ultimate a…
Correlation between Multivariate Datasets, from Inter-Graph Distance computed using Graphical Models Learnt With Uncertainties
Kangrui Wang, Dalia Chakrabarty
We present a method for simultaneous Bayesian learning of the correlation matrix and graphical model of a multivariate dataset, along with uncertainties in each, to subsequently co…
Bayesian Covariance Modelling of Large Tensor-Variate Data Sets Inverse Non-parametric Learning of the Unknown Model Parameter Vector
Kangrui Wang, Dalia Chakrabarty
We present a method for modelling the covariance structure of tensor-variate data, with the ulterior aim of learning an unknown model parameter vector using such data. We express t…
Bayesian Covariance Modelling of Large Tensor-Variate Data Sets Inverse Non-parametric Learning of the Unknown Model Parameter Vector
Kangrui Wang, Dalia Chakrabarty
Tensor-valued data are being encountered increasingly more commonly, in the biological, natural as well as the social sciences. The learning of the unknown model parameter vector g…