Hien Duy Nguyen

According to our database, Hien Duy Nguyen authored at least 46 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Risk Bounds for Mixture Density Estimation on Compact Domains via the <i>h</i>-Lifted Kullback-Leibler Divergence.

Mark Chiu Chong, Hien Duy Nguyen, TrungTin Nguyen

CoRR, 2024

Bayesian Likelihood Free Inference using Mixtures of Experts.

Hien Duy Nguyen, TrungTin Nguyen, Florence Forbes

Proceedings of the International Joint Conference on Neural Networks, 2024

On the Asymptotic Distribution of the Minimum Empirical Risk.

Jacob Westerhout, TrungTin Nguyen, Xin Guo, Hien Duy Nguyen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection.

TrungTin Nguyen, Dung Ngoc Nguyen, Hien Duy Nguyen, Faicel Chamroukhi

Proceedings of the AI 2023: Advances in Artificial Intelligence, 2023

2022

Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors.

Florence Forbes, Hien Duy Nguyen, TrungTin Nguyen, Julyan Arbel

Stat. Comput., 2022

2021

Model independent feature attributions: Shapley values that uncover non-linear dependencies.

Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen

PeerJ Comput. Sci., 2021

A non-asymptotic model selection in block-diagonal mixture of polynomial experts models.

TrungTin Nguyen, Faicel Chamroukhi, Hien Duy Nguyen, Florence Forbes

CoRR, 2021

A non-asymptotic penalization criterion for model selection in mixture of experts models.

TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Florence Forbes

CoRR, 2021

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms.

Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen

IEEE Access, 2021

2020

Mini-batch learning of exponential family finite mixture models.

Hien Duy Nguyen, Florence Forbes, Geoffrey J. McLachlan

Stat. Comput., 2020

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

Jessica J. Bagnall, Andrew T. Jones, Natalie Karavarsamis, Hien D. Nguyen

J. Comput. Soc. Sci., 2020

Shapley Value Confidence Intervals for Attributing Variance Explained.

Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen

Frontiers Appl. Math. Stat., 2020

Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions.

Edoardo Redivo, Hien Duy Nguyen, Mayetri Gupta

Comput. Stat. Data Anal., 2020

An l₁-oracle inequality for the Lasso in mixture-of-experts regression models.

TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Geoffrey J. McLachlan

CoRR, 2020

Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies.

Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen

CoRR, 2020

Approximate Bayesian Computation Via the Energy Statistic.

Hien Duy Nguyen, Julyan Arbel, Hongliang Lü, Florence Forbes

IEEE Access, 2020

k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences.

Daniel Vidali Fryer, Hien D. Nguyen, Pascal Castellazzi

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

2019

Model-based clustering and classification of functional data.

Faicel Chamroukhi, Hien Duy Nguyen

WIREs Data Mining Knowl. Discov., 2019

BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines.

Andrew T. Jones, Jessica Bagnall, Hien Duy Nguyen

J. Open Source Softw., 2019

studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset.

Daniel Vidali Fryer, Hien D. Nguyen, Pierre Orban

J. Open Source Softw., 2019

Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model.

Hien D. Nguyen, Faicel Chamroukhi, Florence Forbes

Neurocomputing, 2019

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

Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen

CoRR, 2019

2018

Practical and theoretical aspects of mixture-of-experts modeling: An overview.

Hien Duy Nguyen, Faicel Chamroukhi

WIREs Data Mining Knowl. Discov., 2018

Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling.

Hien Duy Nguyen, Jeremy F. P. Ullmann, Geoffrey J. McLachlan, Venkatakaushik Voleti, Wenze Li, Elizabeth M. C. Hillman, David C. Reutens, Andrew L. Janke

Stat. Anal. Data Min., 2018

logKDE: log-transformed kernel density estimation.

Andrew T. Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

J. Open Source Softw., 2018

Randomized mixture models for probability density approximation and estimation.

Hien Duy Nguyen, Dianhui Wang, Geoffrey J. McLachlan

Inf. Sci., 2018

A globally convergent algorithm for lasso-penalized mixture of linear regression models.

Luke R. Lloyd-Jones, Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2018

Positive Data Kernel Density Estimation via the LogKDE Package for R.

Hien D. Nguyen, Andrew T. Jones, Geoffrey J. McLachlan

Proceedings of the Data Mining - 16th Australasian Conference, AusDM 2018, Bahrurst, NSW, 2018

2017

An introduction to Majorization-Minimization algorithms for machine learning and statistical estimation.

Hien Duy Nguyen

WIREs Data Mining Knowl. Discov., 2017

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data.

Hien Duy Nguyen, Geoffrey J. McLachlan, Pierre Orban, Pierre Bellec, Andrew L. Janke

Neural Comput., 2017

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

Hien D. Nguyen, Geoffrey J. McLachlan

CoRR, 2017

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

Hien D. Nguyen, Faicel Chamroukhi

CoRR, 2017

A Two-Sample Kolmogorov-Smirnov-Like Test for Big Data.

Hien Duy Nguyen

Proceedings of the Data Mining, 2017

2016

Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines.

Hien Duy Nguyen, Ian A. Wood

IEEE Trans. Neural Networks Learn. Syst., 2016

A Block Minorization-Maximization Algorithm for Heteroscedastic Regression.

Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan

IEEE Signal Process. Lett., 2016

A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines.

Hien Duy Nguyen, Ian A. Wood

Neural Comput., 2016

A Universal Approximation Theorem for Mixture-of-Experts Models.

Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan

Neural Comput., 2016

Mixtures of spatial spline regressions for clustering and classification.

Hien Duy Nguyen, Geoffrey J. McLachlan, Ian A. Wood

Comput. Stat. Data Anal., 2016

Linear mixed models with marginally symmetric nonparametric random effects.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

Maximum likelihood estimation of triangular and polygonal distributions.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

Laplace mixture of linear experts.

Hien Duy Nguyen, Geoffrey J. McLachlan

Comput. Stat. Data Anal., 2016

2015

Maximum likelihood estimation of Gaussian mixture models without matrix operations.

Hien Duy Nguyen, Geoffrey J. McLachlan

Adv. Data Anal. Classif., 2015

2014

False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields.

Hien Duy Nguyen, Geoffrey J. McLachlan, Nicolas Cherbuin, Andrew L. Janke

IEEE Trans. Medical Imaging, 2014

Asymptotic inference for hidden process regression models.

Hien Duy Nguyen, Geoffrey J. McLachlan

Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

2013

Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies.

Hien Duy Nguyen, Andrew L. Janke, Nicolas Cherbuin, Geoffrey J. McLachlan, Perminder S. Sachdev, Kaarin Anstey

Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications, 2013

2012

Variable selection in statistical models using population-based incremental learning with applications to genome-wide association studies.

Hien Duy Nguyen, Ian A. Wood

Proceedings of the IEEE Congress on Evolutionary Computation, 2012