18 citations · 34 across the 11 of their papers we have counts for
11 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…
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Xiaoming Shi, Siqiao Xue, Kangrui Wang +5
Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and hel…
Explicit Planning Helps Language Models in Logical Reasoning
Hongyu Zhao, Kangrui Wang, Mo Yu +1
Language models have been shown to perform remarkably well on a wide range of natural language processing tasks. In this paper, we propose LEAP, a novel system that uses language m…
Trap of Feature Diversity in the Learning of MLPs
Dongrui Liu, Shaobo Wang, Jie Ren +4
In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity…
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…
Multi-resolution Multi-task Gaussian Processes
Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang +1
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution…
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…
Uncertainty in Test Score Data and Classically Defined Reliability of Tests and Test Batteries, using a New Method for Test Dichotomisation
Satyendra Nath Chakrabartty, Kangrui Wang, Dalia Chakrabarty
As with all measurements, the measurement of examinee ability, in terms of scores that the examinee obtains in a test, is also error-ridden. The quantification of such error or unc…