17 citations · 48 across the 20 of their papers we have counts for
25 papers
Riemannian Gradient Descent for Gaussian Mixture Models with unknown diagonal covariances
Romane Giard, Yohann De Castro, Roland Denis +1
This paper investigates the numerical resolution of the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures. We consider its ap…
Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
Romane Giard, Yohann de Castro, Clément Marteau
This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ…
Minimax testing in a statistical inverse problem with unknown operator
Clément Marteau, Theofanis Sapatinas
We study minimax testing in a statistical inverse problem when the associated operator is unknown. In particular, we consider observations from an inverse Gaussian regression model…
FastPart: Over-Parameterized Stochastic Gradient Descent for Sparse optimisation on Measures
Yohann De Castro, Sébastien Gadat, Clément Marteau
This paper presents a novel algorithm that leverages Stochastic Gradient Descent strategies in conjunction with Random Features to augment the scalability of Conic Particle Gradien…
Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data
Louna Alsouki, Laurent Duval, Clément Marteau +2
Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by lo…
SuperMix: Sparse Regularization for Mixtures
Yohann de Castro, Sébastien Gadat, Clément Marteau +1
This paper investigates the statistical estimation of a discrete mixing measure 0 involved in a kernel mixture model. Using some recent advances in l1-regularization over the sp…