Hien D. Nguyen | DeepAI

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Featured Co-authors

Faicel Chamroukhi
33 publications
Geoffrey J. McLachlan
28 publications
Michel A. Kinsy
18 publications
Julyan Arbel
18 publications
Katsumi Inoue
15 publications
Florence Forbes
14 publications
Quoc Hung Ngo
9 publications
Binh Nguyen
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Daniel Fryer
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Dianhui Wang
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Taisuke Sato
6 publications

∙ 02/28/2023

Finite sample inference for empirical Bayesian methods

In recent years, empirical Bayesian (EB) inference has become an attract...

0 Hien D. Nguyen, et al. ∙

∙ 01/26/2023

Ontology-based Solution for Building an Intelligent Searching System on Traffic Law Documents

In this paper, an ontology-based approach is used to organize the knowle...

0 Vuong T. Pham, et al. ∙

∙ 03/19/2021

Order selection with confidence for finite mixture models

The determination of the number of mixture components (the order) of a f...

0 Hien D. Nguyen, et al. ∙

∙ 09/22/2020

An l_1-oracle inequality for the Lasso in mixture-of-experts regression models

Mixture-of-experts (MoE) models are a popular framework for modeling het...

6 TrungTin Nguyen, et al. ∙

∙ 08/22/2020

Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces

The class of location-scale finite mixtures is of enduring interest both...

1 TrungTin Nguyen, et al. ∙

∙ 07/23/2020

A binary-response regression model based on support vector machines

The soft-margin support vector machine (SVM) is a ubiquitous tool for pr...

0 Hien D. Nguyen, et al. ∙

∙ 09/12/2019

Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models

Mixtures-of-Experts models and their maximum likelihood estimation (MLE)...

0 Faïcel Chamroukhi, et al. ∙

∙ 05/14/2019

Approximate Bayesian computation via the energy statistic

Approximate Bayesian computation (ABC) has become an essential part of t...

0 Hien D. Nguyen, et al. ∙

∙ 03/05/2019

Concentration-based confidence intervals for U-statistics

Concentration inequalities have become increasingly popular in machine l...

0 Hien D. Nguyen, et al. ∙

∙ 03/01/2019

Approximation by finite mixtures of continuous density functions that vanish at infinity

Given sufficiently many components, it is often cited that finite mixtur...

0 T Tin Nguyen, et al. ∙

∙ 02/09/2019

Asymptotic normality of the time-domain generalized least squares estimator for linear regression models

In linear models, the generalized least squares (GLS) estimator is appli...

0 Hien D. Nguyen, et al. ∙

∙ 01/26/2019

On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables

We investigate the sub-Gaussian property for almost surely bounded rando...

0 Julyan Arbel, et al. ∙

∙ 11/28/2018

Partial Evaluation of Logic Programs in Vector Spaces

In this paper, we introduce methods of encoding propositional logic prog...

0 Chiaki Sakama, et al. ∙

∙ 11/18/2018

The fully-visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016

After the 2016 double dissolution election, the 45th Australian Parliame...

0 Jessica J. Bagnall, et al. ∙

∙ 05/11/2018

False Discovery Rate Control Under Reduced Precision Computation

The mitigation of false positives is an important issue when conducting ...

0 Hien D. Nguyen, et al. ∙

∙ 04/23/2018

Positive data kernel density estimation via the logKDE package for R

Kernel density estimators (KDEs) are ubiquitous tools for nonparametric ...

0 Andrew T. Jones, et al. ∙

∙ 04/23/2018

Log-transformed kernel density estimation for positive data

Kernel density estimators (KDEs) are ubiquitous tools for nonpara- metri...

0 Andrew T. Jones, et al. ∙

∙ 04/23/2018

Randomized Mixture Models for Probability Density Approximation and Estimation

Randomized neural networks (NNs) are an interesting alternative to conve...

0 Hien D. Nguyen, et al. ∙

∙ 03/01/2018

Model-Based Clustering and Classification of Functional Data

The problem of complex data analysis is a central topic of modern statis...

0 Faicel Chamroukhi, et al. ∙

∙ 09/14/2017

A Novel Algorithm for Clustering of Data on the Unit Sphere via Mixture Models

A new maximum approximate likelihood (ML) estimation algorithm for the m...

0 Hien D. Nguyen, et al. ∙

∙ 07/12/2017

An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling

Mixture-of-experts (MoE) models are a powerful paradigm for modeling of ...

1 Hien D. Nguyen, et al. ∙

∙ 06/29/2017

Fast Processing of Large Graph Applications Using Asynchronous Architecture

Graph algorithms and techniques are increasingly being used in scientifi...

0 Michel A. Kinsy, et al. ∙

∙ 05/12/2017

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach

Support vector machines (SVMs) are an important tool in modern data anal...

0 Hien D. Nguyen, et al. ∙

∙ 11/12/2016

An Introduction to MM Algorithms for Machine Learning and Statistical

MM (majorization--minimization) algorithms are an increasingly popular t...

0 Hien D. Nguyen, et al. ∙

∙ 02/11/2016

A Universal Approximation Theorem for Mixture of Experts Models

The mixture of experts (MoE) model is a popular neural network architect...

0 Hien D. Nguyen, et al. ∙