172 citations · 664 across the 34 of their papers we have counts for
37 papers
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting
Thien Pham, Angelo Furno, Faïcel Chamroukhi +1
This paper presents an advanced Federated Learning (FL) framework for forecasting complex spatiotemporal data, improving upon recent state-of-the-art models. In the proposed approa…
Optimal Transport Aggregation for Distributed Mixture-of-Experts
Faïcel Chamroukhi, Nhat Thien Pham
This paper considers the setting in which local Mixture-of-experts (MoE) models are fitted independently on distributed datasets and must be combined into a single global MoE model…
Data-driven Reachability using Christoffel Functions and Conformal Prediction
Abdelmouaiz Tebjou, Goran Frehse, Faïcel Chamroukhi
An important mathematical tool in the analysis of dynamical systems is the approximation of the reach set, i.e., the set of states reachable after a given time from a given initial…
Functional mixture-of-experts for classification
Nhat Thien Pham, Faicel Chamroukhi
We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions. It consists of a ME model in which both the gating ne…
Functional Mixtures-of-Experts
Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang +1
We consider the statistical analysis of heterogeneous data for prediction in situations where the observations include functions, typically time series. We extend the modeling with…
Spectral image clustering on dual-energy CT scans using functional regression mixtures
Segolene Brivet, Faicel Chamroukhi, Mark Coates +2
Dual-energy computed tomography (DECT) is an advanced CT scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction…
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
TrungTin Nguyen, Faicel Chamroukhi, Hien Duy Nguyen +1
Model selection, via penalized likelihood type criteria, is a standard task in many statistical inference and machine learning problems. Progress has led to deriving criteria with…
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models
TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi +1
Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their flexibility and efficiency. In this w…
Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models
Hien Duy Nguyen, TrungTin Nguyen, Faicel Chamroukhi +1
Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving densen…
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
TrungTin Nguyen, Hien D Nguyen, Faicel Chamroukhi +1
We investigate the estimation properties of the mixture of experts (MoE) model in a high-dimensional setting, where the number of predictors is much larger than the sample size, an…
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
TrungTin Nguyen, Faicel Chamroukhi, Hien D Nguyen +1
The class of location-scale finite mixtures is of enduring interest both from applied and theoretical perspectives of probability and statistics. We prove the following results: to…
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen
Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature. They a…
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Bao Tuyen Huynh, Thin Nguyen-Van, Faicel Chamroukhi +1
Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predictors, and are widely used for regressi…
Approximation by finite mixtures of continuous density functions that vanish at infinity
T Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi +1
Given sufficiently many components, it is often cited that finite mixture models can approximate any other probability density function (pdf) to an arbitrary degree of accuracy. Un…
Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
Faicel Chamroukhi, Bao-Tuyen Huynh
Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally…
Model-Based Clustering and Classification of Functional Data
Faicel Chamroukhi, Hien D. Nguyen
The problem of complex data analysis is a central topic of modern statistical science and learning systems and is becoming of broader interest with the increasing prevalence of hig…
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Hien D. Nguyen, Faicel Chamroukhi
Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data arising from complex data generating processes (DGPs). In this article, we demonstrate how different Mo…
Robust mixture of experts modeling using the distribution
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification, and clustering. For regression and cluster analyses of continuous…
Robust mixture of experts modeling using the skew distribution
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework in the fields of statistics and machine learning for modeling heterogeneity in data for regression, classification and clustering. M…
Bayesian mixtures of spatial spline regressions
Faicel Chamroukhi
This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model wit…
Non-Normal Mixtures of Experts
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification and clustering. For continuous data which we consider here in the…
Dirichlet Process Parsimonious Mixtures for clustering
Faicel Chamroukhi, Marius Bartcus, Hervé Glotin
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular…
Unsupervised learning of regression mixture models with unknown number of components
Faicel Chamroukhi
Regression mixture models are widely studied in statistics, machine learning and data analysis. Fitting regression mixtures is challenging and is usually performed by maximum likel…
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Faicel Chamroukhi, Allou Samé, Patrice Aknin +1
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governe…