
Order selection with confidence for finite mixture models
The determination of the number of mixture components (the order) of a f...
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An l_1oracle inequality for the Lasso in mixtureofexperts regression models
Mixtureofexperts (MoE) models are a popular framework for modeling het...
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Approximation of probability density functions via locationscale finite mixtures in Lebesgue spaces
The class of locationscale finite mixtures is of enduring interest both...
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A binaryresponse regression model based on support vector machines
The softmargin support vector machine (SVM) is a ubiquitous tool for pr...
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Regularized Estimation and Feature Selection in Mixtures of GaussianGated Experts Models
MixturesofExperts models and their maximum likelihood estimation (MLE)...
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Approximate Bayesian computation via the energy statistic
Approximate Bayesian computation (ABC) has become an essential part of t...
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Concentrationbased confidence intervals for Ustatistics
Concentration inequalities have become increasingly popular in machine l...
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Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixtur...
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Asymptotic normality of the timedomain generalized least squares estimator for linear regression models
In linear models, the generalized least squares (GLS) estimator is appli...
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On strict subGaussianity, optimal proxy variance and symmetry for bounded random variables
We investigate the subGaussian property for almost surely bounded rando...
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Partial Evaluation of Logic Programs in Vector Spaces
In this paper, we introduce methods of encoding propositional logic prog...
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The fullyvisible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016
After the 2016 double dissolution election, the 45th Australian Parliame...
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False Discovery Rate Control Under Reduced Precision Computation
The mitigation of false positives is an important issue when conducting ...
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Positive data kernel density estimation via the logKDE package for R
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric ...
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Logtransformed kernel density estimation for positive data
Kernel density estimators (KDEs) are ubiquitous tools for nonpara metri...
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Randomized Mixture Models for Probability Density Approximation and Estimation
Randomized neural networks (NNs) are an interesting alternative to conve...
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ModelBased Clustering and Classification of Functional Data
The problem of complex data analysis is a central topic of modern statis...
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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...
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An Introduction to the Practical and Theoretical Aspects of MixtureofExperts Modeling
Mixtureofexperts (MoE) models are a powerful paradigm for modeling of ...
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Fast Processing of Large Graph Applications Using Asynchronous Architecture
Graph algorithms and techniques are increasingly being used in scientifi...
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IterativelyReweighted LeastSquares Fitting of Support Vector Machines: A MajorizationMinimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data anal...
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An Introduction to MM Algorithms for Machine Learning and Statistical
MM (majorizationminimization) algorithms are an increasingly popular t...
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A Universal Approximation Theorem for Mixture of Experts Models
The mixture of experts (MoE) model is a popular neural network architect...
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Hien D. Nguyen
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