216 citations · 552 across the 14 of their papers we have counts for
17 papers
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…
Leveraging 3D Information in Unsupervised Brain MRI Segmentation
Benjamin Lambert, Maxime Louis, Senan Doyle +3
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated…
Rigid and Articulated Point Registration with Expectation Conditional Maximization
Radu Horaud, Florence Forbes, Manuel Yguel +2
This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration. The problem is recast into a missing data framework where unknown…
Conjugate Mixture Models for Clustering Multimodal Data
Vasil Khalidov, Florence Forbes, Radu Horaud
The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Observations from different modalities are not necessarily ali…
Approximate Bayesian computation via the energy statistic
Hien D. Nguyen, Julyan Arbel, Hongliang Lü +1
Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely u…
Mini-batch learning of exponential family finite mixture models
H D Nguyen, F Forbes, G J McLachlan
Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectatio…
Rectified binaural ratio: A complex T-distributed feature for robust sound localization
Antoine Deleforge, Florence Forbes
Most existing methods in binaural sound source localization rely on some kind of aggregation of phase-and level-difference cues in the time-frequency plane. While different ag-greg…
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes +1
Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play…
Physiologically Informed Bayesian Analysis of ASL fMRI Data
Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda +2
Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. I…
Hemodynamically informed parcellation of cerebral FMRI data
Aina Frau-Pascual, Thomas Vincent, Florence Forbes +1
Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic…
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
Antoine Deleforge, Florence Forbes, Sileye Ba +1
Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learnin…
Clustering using skewed multivariate heavy tailed distributions with flexible tail behaviour
Darren Wraith, Florence Forbes
The family of location and scale mixtures of Gaussians has the ability to generate a number of flexible distributional forms. It nests as particular cases several important asymmet…
Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds
Antoine Deleforge, Florence Forbes, Radu Horaud
In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of…
Spatial risk mapping for rare disease with hidden Markov fields and variational EM
Florence Forbes, Myriam Charras-Garrido, Lamiae Azizi +2
Current risk mapping models for pooled data focus on the estimated risk for each geographical unit. A risk classification, that is, grouping of geographical units with similar risk…
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables
Antoine Deleforge, Florence Forbes, Radu Horaud
In this work we address the problem of approximating high-dimensional data with a low-dimensional representation. We make the following contributions. We propose an inverse regress…
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
Lotfi Chaari, Thomas Vincent, Florence Forbes +2
In standard clinical within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic r…