Faicel Chamroukhi

Orcid: 0000-0002-5894-3103

According to our database1, Faicel Chamroukhi authored at least 52 papers between 2009 and 2024.

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

Timeline

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Bibliography

2024
Functional mixtures-of-experts.
Stat. Comput., June, 2024

AITA: AI trustworthiness assessment.
AI Ethics, February, 2024

2023
Distributed Learning of Mixtures of Experts.
CoRR, 2023

Data-driven Reachability using Christoffel Functions and Conformal Prediction.
Proceedings of the Conformal and Probabilistic Prediction with Applications, 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
Functional mixture-of-experts for classification.
CoRR, 2022

Spectral image clustering on dual-energy CT scans using functional regression mixtures.
CoRR, 2022

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

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

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

Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models.
CoRR, 2019

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

Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models.
CoRR, 2018

Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Regularised maximum-likelihood inference of mixture of experts for regression and clustering.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model.
Proceedings of the Multimedia Tools and Applications for Environmental & Biodiversity Informatics, 2018

2017
Skew t mixture of experts.
Neurocomputing, 2017

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

2016
Robust mixture of experts modeling using the t distribution.
Neural Networks, 2016

Robust mixture of experts modeling using the skew $t$ distribution.
CoRR, 2016

Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation.
J. Classif., 2016

GTM Mixture through time for sequential data.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Skew-normal Mixture of Experts.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Bayesian mixture of spatial spline regressions.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015
Physical Human Activity Recognition Using Wearable Sensors.
Sensors, 2015

Dirichlet Process Parsimonious Mixtures for clustering.
CoRR, 2015

Bayesian mixtures of spatial spline regressions.
CoRR, 2015

Non-Normal Mixtures of Experts.
CoRR, 2015

Hierarchical Dirichlet Process Hidden Markov Model for unsupervised bioacoustic analysis.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014
Unsupervised learning of regression mixture models with unknown number of components.
CoRR, 2014

Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering.
Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Bayesian non-parametric parsimonious clustering.
Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013
An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression.
IEEE Trans Autom. Sci. Eng., 2013

Joint segmentation of multivariate time series with hidden process regression for human activity recognition.
Neurocomputing, 2013

Model-based functional mixture discriminant analysis with hidden process regression for curve classification.
Neurocomputing, 2013

Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time.
CoRR, 2013

Modèle à processus latent et algorithme EM pour la régression non linéaire.
CoRR, 2013

Piecewise regression mixture for simultaneous curve clustering and optimal segmentation.
CoRR, 2013

Robust EM algorithm for model-based curve clustering.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

2012
Mixture model-based functional discriminant analysis for curve classification.
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Supervised and unsupervised classification approaches for human activity recognition using body-mounted sensors.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Functional Mixture Discriminant Analysis with hidden process regression for curve classification.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011
Model-based clustering and segmentation of time series with changes in regime.
Adv. Data Anal. Classif., 2011

Model-based clustering with Hidden Markov Model regression for time series with regime changes.
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

2010
A hidden process regression model for functional data description. Application to curve discrimination.
Neurocomputing, 2010

2009
Time series modeling by a regression approach based on a latent process.
Neural Networks, 2009

A regression model with a hidden logistic process for feature extraction from time series.
Proceedings of the International Joint Conference on Neural Networks, 2009

Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context.
Proceedings of the International Conference on Machine Learning and Applications, 2009

A regression model with a hidden logistic process for signal parametrization.
Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009


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