16 citations · 69 across the 27 of their papers we have counts for
4 papers · 1 filter
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…
Fenchel-Young Duality Gaps: Certified Early Stopping for Regularized Inverse Problems
Pierre-Cyril Aubin-Frankowski, Yohann de Castro
We study computable error bounds and certified early stopping for regularized inverse problems, where a data-fidelity term is traded against a regularizer. The analysis relies on a…
Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process
Yohann De Castro, Sébastien Gadat, Clément Marteau
We investigate the global optimization of the objective function arising in continuous sparse regression, specifically the Beurling LASSO (BLASSO), over the space of measures. Whil…
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…