Hien Duy Nguyen

Orcid: 0000-0002-9958-432X

Affiliations:
  • La Trobe University, Bundoora, Australia
  • The University of Queensland, St. Lucia, QLD, Australia (former)


According to our database1, Hien Duy Nguyen authored at least 47 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
Risk Bounds for Mixture Density Estimation on Compact Domains via the <i>h</i>-Lifted Kullback-Leibler Divergence.
CoRR, 2024

Bayesian Likelihood Free Inference using Mixtures of Experts.
Proceedings of the International Joint Conference on Neural Networks, 2024

On the Asymptotic Distribution of the Minimum Empirical Risk.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Beyond Factualism: A Study of LLM Calibration Through the Lens of Conversational Emotion Recognition.
Proceedings of the AI 2024: Advances in Artificial Intelligence, 2024

2023
A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection.
Proceedings of the AI 2023: Advances in Artificial Intelligence, 2023

2022
Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors.
Stat. Comput., 2022

2021
Model independent feature attributions: Shapley values that uncover non-linear dependencies.
PeerJ Comput. Sci., 2021

A non-asymptotic model selection in block-diagonal mixture of polynomial experts models.
CoRR, 2021

A non-asymptotic penalization criterion for model selection in mixture of experts models.
CoRR, 2021

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms.
IEEE Access, 2021

2020
Mini-batch learning of exponential family finite mixture models.
Stat. Comput., 2020

The fully visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016.
J. Comput. Soc. Sci., 2020

Shapley Value Confidence Intervals for Attributing Variance Explained.
Frontiers Appl. Math. Stat., 2020

Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions.
Comput. Stat. Data Anal., 2020

An l<sub>1</sub>-oracle inequality for the Lasso in mixture-of-experts regression models.
CoRR, 2020

Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies.
CoRR, 2020

Approximate Bayesian Computation Via the Energy Statistic.
IEEE Access, 2020

k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

2019
Model-based clustering and classification of functional data.
WIREs Data Mining Knowl. Discov., 2019

BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines.
J. Open Source Softw., 2019

studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset.
J. Open Source Softw., 2019

Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model.
Neurocomputing, 2019

Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models.
CoRR, 2019

2018
Practical and theoretical aspects of mixture-of-experts modeling: An overview.
WIREs Data Mining Knowl. Discov., 2018

Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling.
Stat. Anal. Data Min., 2018

logKDE: log-transformed kernel density estimation.
J. Open Source Softw., 2018

Randomized mixture models for probability density approximation and estimation.
Inf. Sci., 2018

A globally convergent algorithm for lasso-penalized mixture of linear regression models.
Comput. Stat. Data Anal., 2018

Positive Data Kernel Density Estimation via the LogKDE Package for R.
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.
WIREs Data Mining Knowl. Discov., 2017

Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data.
Neural Comput., 2017

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach.
CoRR, 2017

An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling.
CoRR, 2017

A Two-Sample Kolmogorov-Smirnov-Like Test for Big Data.
Proceedings of the Data Mining, 2017

2016
Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines.
IEEE Trans. Neural Networks Learn. Syst., 2016

A Block Minorization-Maximization Algorithm for Heteroscedastic Regression.
IEEE Signal Process. Lett., 2016

A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines.
Neural Comput., 2016

A Universal Approximation Theorem for Mixture-of-Experts Models.
Neural Comput., 2016

Mixtures of spatial spline regressions for clustering and classification.
Comput. Stat. Data Anal., 2016

Linear mixed models with marginally symmetric nonparametric random effects.
Comput. Stat. Data Anal., 2016

Maximum likelihood estimation of triangular and polygonal distributions.
Comput. Stat. Data Anal., 2016

Laplace mixture of linear experts.
Comput. Stat. Data Anal., 2016

2015
Maximum likelihood estimation of Gaussian mixture models without matrix operations.
Adv. Data Anal. Classif., 2015

2014
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields.
IEEE Trans. Medical Imaging, 2014

Asymptotic inference for hidden process regression models.
Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

2013
Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies.
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.
Proceedings of the IEEE Congress on Evolutionary Computation, 2012


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