8 citations · 11 across the 10 of their papers we have counts for
13 papers
Bayesian Sphere-on-Sphere Regression with Optimal Transport Maps
Tin Lok James Ng, Kwok-Kun Kwong, Jiakun Liu +1
Spherical regression, in which both covariates and responses lie on the sphere, arises in many scientific applications and has attracted considerable methodological attention in re…
Mixture Modeling with Normalizing Flows for Spherical Density Estimation
Tin Lok James Ng, Andrew Zammit-Mangion
Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years. Many flexible families of norma…
Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport
Tin Lok James Ng, Andrew Zammit-Mangion
Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistic…
Fact Check: Analyzing Financial Events from Multilingual News Sources
Linyi Yang, Tin Lok James Ng, Barry Smyth +1
The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights an…
Posterior Regularization on Bayesian Hierarchical Mixture Clustering
Weipeng Huang, Tin Lok James Ng, Nishma Laitonjam +1
Bayesian hierarchical mixture clustering (BHMC) improves traditionalBayesian hierarchical clustering by replacing conventional Gaussian-to-Gaussian kernels with a Hierarchical Diri…
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Linyi Yang, Eoin M. Kenny, Tin Lok James Ng +3
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intellige…
Penalized Maximum Likelihood Estimator for Mixture of von Mises-Fisher Distributions
Tin Lok James Ng
The von Mises-Fisher distribution is one of the most widely used probability distributions to describe directional data. Finite mixtures of von Mises-Fisher distributions have foun…
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport
Tin Lok James Ng, Andrew Zammit-Mangion
Non-homogeneous Poisson processes are used in a wide range of scientific disciplines, ranging from the environmental sciences to the health sciences. Often, the central object of i…
Universal Approximation on the Hypersphere
Tin Lok James Ng, Kwok-Kun Kwong
It is well known that any continuous probability density function on can be approximated arbitrarily well by a finite mixture of normal distributions, provided that…
Deep Compositional Spatial Models
Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu +1
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes ma…
Model-based clustering for random hypergraphs
Tin Lok James Ng, Thomas Brendan Murphy
A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension…
Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel
Tin Lok James Ng, Thomas Brendan Murphy, Ted Westling +2
Dáil Éireann is the principal chamber of the Irish parliament. The 31st Dáil Éireann is the principal chamber of the Irish parliament. The 31st Dáil was in session from March 11th,…
Multiresolution network models
Bailey K. Fosdick, Tyler H. McCormick, Thomas Brendan Murphy +2
Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of…