Variable selection in statistical models using population-based incremental learning with applications to genome-wide association studies. - Hien Duy Nguyen; Ian A. Wood (IEEE Congress on Evolutionary Computation, 2012)

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 (DICTA, 2013)

Asymptotic inference for hidden process regression models. - Hien Duy Nguyen; Geoffrey J. McLachlan (SSP, 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)

Maximum likelihood estimation of Gaussian mixture models without matrix operations. - Hien Duy Nguyen; Geoffrey J. McLachlan (Adv. Data Anal. Classif., 2015)

Laplace mixture of linear experts. - 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)

Linear mixed models with marginally symmetric nonparametric random effects. - Hien Duy Nguyen; Geoffrey J. McLachlan (Comput. Stat. Data Anal., 2016)

Mixtures of spatial spline regressions for clustering and classification. - Hien Duy Nguyen; Geoffrey J. McLachlan; Ian A. Wood (Comput. Stat. Data Anal., 2016)

A Universal Approximation Theorem for Mixture-of-Experts Models. - Hien Duy Nguyen; Luke R. Lloyd-Jones; Geoffrey J. McLachlan (Neural Comput., 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 Block Minorization-Maximization Algorithm for Heteroscedastic Regression. - Hien Duy Nguyen; Luke R. Lloyd-Jones; Geoffrey J. McLachlan (IEEE Signal Process. Lett., 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 Two-Sample Kolmogorov-Smirnov-Like Test for Big Data. - Hien Duy Nguyen (AusDM, 2017)

An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling. - Hien D. Nguyen 001; Faicel Chamroukhi (CoRR, 2017)

Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach. - Hien D. Nguyen 001; Geoffrey J. McLachlan (CoRR, 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)

An introduction to Majorization-Minimization algorithms for machine learning and statistical estimation. - Hien Duy Nguyen (WIREs Data Mining Knowl. Discov., 2017)

Positive Data Kernel Density Estimation via the LogKDE Package for R. - Hien D. Nguyen 001; Andrew T. Jones; Geoffrey J. McLachlan (AusDM, 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)

Randomized mixture models for probability density approximation and estimation. - Hien Duy Nguyen; Dianhui Wang; Geoffrey J. McLachlan (Inf. Sci., 2018)

logKDE: log-transformed kernel density estimation. - Andrew T. Jones; Hien Duy Nguyen; Geoffrey J. McLachlan (J. Open Source Softw., 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)

Practical and theoretical aspects of mixture-of-experts modeling: An overview. - Hien Duy Nguyen; Faicel Chamroukhi (WIREs Data Mining Knowl. Discov., 2018)

Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models. - Faïcel Chamroukhi; Florian Lecocq; Hien D. Nguyen 001 (CoRR, 2019)

Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model. - Hien D. Nguyen 001; Faicel Chamroukhi; Florence Forbes (Neurocomputing, 2019)

studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset. - Daniel Vidali Fryer; Hien D. Nguyen 001; Pierre Orban (J. Open Source Softw., 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)

Model-based clustering and classification of functional data. - Faicel Chamroukhi; Hien Duy Nguyen (WIREs Data Mining Knowl. Discov., 2019)

k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences. - Daniel Vidali Fryer; Hien D. Nguyen 001; Pascal Castellazzi (SSCI, 2020)

Approximate Bayesian Computation Via the Energy Statistic. - Hien Duy Nguyen; Julyan Arbel; Hongliang Lü; Florence Forbes (IEEE Access, 2020)

Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (CoRR, 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)

Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions. - Edoardo Redivo; Hien Duy Nguyen; Mayetri Gupta (Comput. Stat. Data Anal., 2020)

Shapley Value Confidence Intervals for Attributing Variance Explained. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (Frontiers Appl. Math. Stat., 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 001 (J. Comput. Soc. Sci., 2020)

Mini-batch learning of exponential family finite mixture models. - Hien Duy Nguyen; Florence Forbes; Geoffrey J. McLachlan (Stat. Comput., 2020)

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (IEEE Access, 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)

A non-asymptotic model selection in block-diagonal mixture of polynomial experts models. - TrungTin Nguyen; Faicel Chamroukhi; Hien Duy Nguyen; Florence Forbes (CoRR, 2021)

Model independent feature attributions: Shapley values that uncover non-linear dependencies. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (PeerJ Comput. Sci., 2021)

Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. - Florence Forbes; Hien Duy Nguyen; TrungTin Nguyen; Julyan Arbel (Stat. Comput., 2022)

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 (AI (2), 2023)

Beyond Factualism: A Study of LLM Calibration Through the Lens of Conversational Emotion Recognition. - Samad Roohi; Richard Skarbez; Hien Duy Nguyen (AI (1), 2024)

On the Asymptotic Distribution of the Minimum Empirical Risk. - Jacob Westerhout; TrungTin Nguyen; Xin Guo; Hien Duy Nguyen (ICML, 2024)

Bayesian Likelihood Free Inference using Mixtures of Experts. - Hien Duy Nguyen; TrungTin Nguyen; Florence Forbes (IJCNN, 2024)

Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback-Leibler Divergence. - Mark Chiu Chong; Hien Duy Nguyen; TrungTin Nguyen (CoRR, 2024)

Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback-Leibler Divergence. - Mark Chiu Chong; Hien Duy Nguyen; TrungTin Nguyen (Trans. Mach. Learn. Res., 2024)