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ORCIDHien Duy Nguyen
Person information
- affiliation: La Trobe University, Bundoora, Australia
- affiliation (former): The University of Queensland, St. Lucia, QLD, Australia
Other persons with the same name
- Hien D. Nguyen — disambiguation page
- Hien D. Nguyen 0002
![[0000-0002-8527-0602]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-8527-0602")
— University of Information Technology, Ho Chi Minh City, Vietnam
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2020 – today
- 2024
[c10]Samad Roohi, Richard Skarbez, Hien Duy Nguyen:
Beyond Factualism: A Study of LLM Calibration Through the Lens of Conversational Emotion Recognition. AI (1) 2024: 198-212- [c9]Jacob Westerhout, TrungTin Nguyen, Xin Guo, Hien Duy Nguyen:
On the Asymptotic Distribution of the Minimum Empirical Risk. ICML 2024 - [c8]Hien Duy Nguyen, TrungTin Nguyen, Florence Forbes:
Bayesian Likelihood Free Inference using Mixtures of Experts. IJCNN 2024: 1-8 - [i11]Mark Chiu Chong, Hien Duy Nguyen, TrungTin Nguyen:
Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback-Leibler Divergence. CoRR abs/2404.12586 (2024) - 2023
- [c7]TrungTin Nguyen, Dung Ngoc Nguyen, Hien Duy Nguyen, Faicel Chamroukhi:
A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection. AI (2) 2023: 234-245 - 2022
- [j29]Florence Forbes, Hien Duy Nguyen, TrungTin Nguyen, Julyan Arbel:
Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. Stat. Comput. 32(5): 85 (2022) - 2021
- [j28]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley Values for Feature Selection: The Good, the Bad, and the Axioms. IEEE Access 9: 144352-144360 (2021) - [j27]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Model independent feature attributions: Shapley values that uncover non-linear dependencies. PeerJ Comput. Sci. 7: e582 (2021) - [i10]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley values for feature selection: The good, the bad, and the axioms. CoRR abs/2102.10936 (2021) - [i9]TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Florence Forbes:
A non-asymptotic penalization criterion for model selection in mixture of experts models. CoRR abs/2104.02640 (2021) - [i8]TrungTin Nguyen, Faicel Chamroukhi, Hien Duy Nguyen, Florence Forbes:
A non-asymptotic model selection in block-diagonal mixture of polynomial experts models. CoRR abs/2104.08959 (2021) - 2020
- [j26]Hien Duy Nguyen, Julyan Arbel, Hongliang Lü, Florence Forbes:
Approximate Bayesian Computation Via the Energy Statistic. IEEE Access 8: 131683-131698 (2020) - [j25]Edoardo Redivo, Hien Duy Nguyen, Mayetri Gupta:
Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions. Comput. Stat. Data Anal. 152: 107040 (2020) - [j24]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Shapley Value Confidence Intervals for Attributing Variance Explained. Frontiers Appl. Math. Stat. 6: 587199 (2020) - [j23]Jessica J. Bagnall, Andrew T. Jones, Natalie Karavarsamis, Hien D. Nguyen:
The fully visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016. J. Comput. Soc. Sci. 3(1): 55-81 (2020) - [j22]Hien Duy Nguyen, Florence Forbes, Geoffrey J. McLachlan:
Mini-batch learning of exponential family finite mixture models. Stat. Comput. 30(4): 731-748 (2020) - [c6]Daniel Vidali Fryer, Hien D. Nguyen, Pascal Castellazzi:
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences. SSCI 2020: 1045-1050 - [i7]Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen:
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies. CoRR abs/2007.06011 (2020) - [i6]Daniel Vidali Fryer, Hien D. Nguyen, Pascal Castellazzi:
k-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences. CoRR abs/2008.03454 (2020) - [i5]TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Geoffrey J. McLachlan:
An l1-oracle inequality for the Lasso in mixture-of-experts regression models. CoRR abs/2009.10622 (2020)
2010 – 2019
- 2019
- [j21]Hien D. Nguyen, Faicel Chamroukhi, Florence Forbes:
Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model. Neurocomputing 366: 208-214 (2019) - [j20]Andrew T. Jones, Jessica Bagnall, Hien Duy Nguyen:
BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines. J. Open Source Softw. 4(34): 1193 (2019) - [j19]Daniel Vidali Fryer, Hien D. Nguyen, Pierre Orban:
studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset. J. Open Source Softw. 4(40): 1587 (2019) - [j18]Faicel Chamroukhi, Hien Duy Nguyen:
Model-based clustering and classification of functional data. WIREs Data Mining Knowl. Discov. 9(4) (2019) - [i4]Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen:
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models. CoRR abs/1909.05494 (2019) - 2018
- [j17]Luke R. Lloyd-Jones, Hien Duy Nguyen, Geoffrey J. McLachlan:
A globally convergent algorithm for lasso-penalized mixture of linear regression models. Comput. Stat. Data Anal. 119: 19-38 (2018) - [j16]Hien Duy Nguyen, Dianhui Wang, Geoffrey J. McLachlan:
Randomized mixture models for probability density approximation and estimation. Inf. Sci. 467: 135-148 (2018) - [j15]Andrew T. Jones, Hien Duy Nguyen, Geoffrey J. McLachlan:
logKDE: log-transformed kernel density estimation. J. Open Source Softw. 3(28): 870 (2018) - [j14]Hien Duy Nguyen, Jeremy F. P. Ullmann, Geoffrey J. McLachlan, Venkatakaushik Voleti, Wenze Li, Elizabeth M. C. Hillman, David C. Reutens, Andrew L. Janke:
Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Stat. Anal. Data Min. 11(1): 5-16 (2018) - [j13]Hien Duy Nguyen, Faicel Chamroukhi:
Practical and theoretical aspects of mixture-of-experts modeling: An overview. WIREs Data Mining Knowl. Discov. 8(4) (2018) - [c5]Hien D. Nguyen, Andrew T. Jones, Geoffrey J. McLachlan:
Positive Data Kernel Density Estimation via the LogKDE Package for R. AusDM 2018: 269-280 - [i3]Faicel Chamroukhi, Hien D. Nguyen:
Model-Based Clustering and Classification of Functional Data. CoRR abs/1803.00276 (2018) - 2017
- [j12]Hien Duy Nguyen, Geoffrey J. McLachlan, Pierre Orban, Pierre Bellec, Andrew L. Janke:
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. Neural Comput. 29(4): 990-1020 (2017) - [j11]Hien Duy Nguyen:
An introduction to Majorization-Minimization algorithms for machine learning and statistical estimation. WIREs Data Mining Knowl. Discov. 7(2) (2017) - [c4]Hien Duy Nguyen:
A Two-Sample Kolmogorov-Smirnov-Like Test for Big Data. AusDM 2017: 89-106 - [i2]Hien D. Nguyen, Geoffrey J. McLachlan:
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach. CoRR abs/1705.04651 (2017) - [i1]Hien D. Nguyen, Faicel Chamroukhi:
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling. CoRR abs/1707.03538 (2017) - 2016
- [j10]Hien Duy Nguyen, Geoffrey J. McLachlan, Ian A. Wood:
Mixtures of spatial spline regressions for clustering and classification. Comput. Stat. Data Anal. 93: 76-85 (2016) - [j9]Hien Duy Nguyen, Geoffrey J. McLachlan:
Laplace mixture of linear experts. Comput. Stat. Data Anal. 93: 177-191 (2016) - [j8]Hien Duy Nguyen, Geoffrey J. McLachlan:
Maximum likelihood estimation of triangular and polygonal distributions. Comput. Stat. Data Anal. 102: 23-36 (2016) - [j7]Hien Duy Nguyen, Geoffrey J. McLachlan:
Linear mixed models with marginally symmetric nonparametric random effects. Comput. Stat. Data Anal. 103: 151-169 (2016) - [j6]Hien Duy Nguyen, Ian A. Wood:
A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines. Neural Comput. 28(3): 485-492 (2016) - [j5]Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan:
A Universal Approximation Theorem for Mixture-of-Experts Models. Neural Comput. 28(12): 2585-2593 (2016) - [j4]Hien Duy Nguyen, Luke R. Lloyd-Jones, Geoffrey J. McLachlan:
A Block Minorization-Maximization Algorithm for Heteroscedastic Regression. IEEE Signal Process. Lett. 23(8): 1131-1135 (2016) - [j3]Hien Duy Nguyen, Ian A. Wood:
Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines. IEEE Trans. Neural Networks Learn. Syst. 27(4): 897-902 (2016) - 2015
- [j2]Hien Duy Nguyen, Geoffrey J. McLachlan:
Maximum likelihood estimation of Gaussian mixture models without matrix operations. Adv. Data Anal. Classif. 9(4): 371-394 (2015) - 2014
- [j1]Hien Duy Nguyen, Geoffrey J. McLachlan, Nicolas Cherbuin, Andrew L. Janke:
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields. IEEE Trans. Medical Imaging 33(8): 1735-1748 (2014) - [c3]Hien Duy Nguyen, Geoffrey J. McLachlan:
Asymptotic inference for hidden process regression models. SSP 2014: 256-259 - 2013
- [c2]Hien Duy Nguyen, Andrew L. Janke, Nicolas Cherbuin, Geoffrey J. McLachlan, Perminder S. Sachdev, Kaarin Anstey:
Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies. DICTA 2013: 1-8 - 2012
- [c1]Hien Duy Nguyen, Ian A. Wood:
Variable selection in statistical models using population-based incremental learning with applications to genome-wide association studies. IEEE Congress on Evolutionary Computation 2012: 1-8
Coauthor Index
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