36 citations · 83 across the 24 of their papers we have counts for
27 papers
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…
Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions
Marelys Crespo Navas, Sébastien Gadat, Xavier Gendre
In this paper, we investigate a continuous time version of the Stochastic Langevin Monte Carlo method, introduced in [WT11], that incorporates a stochastic sampling step inside the…
A stochastic Gauss-Newton algorithm for regularized semi-discrete optimal transport
Bernard Bercu, Jérémie Bigot, Sébastien Gadat +1
We introduce a new second order stochastic algorithm to estimate the entropically regularized optimal transport cost between two probability measures. The source measure can be arb…
Asymptotic study of stochastic adaptive algorithm in non-convex landscape
Sébastien Gadat, Ioana Gavra
This paper studies some asymptotic properties of adaptive algorithms widely used in optimization and machine learning, and among them Adagrad and Rmsprop, which are involved in mos…
On the cost of Bayesian posterior mean strategy for log-concave models
Sébastien Gadat, Fabien Panloup, Clément Pellegrini
In this paper, we investigate the problem of computing Bayesian estimators using Langevin Monte-Carlo type approximation. The novelty of this paper is to consider together the stat…
Non asymptotic controls on a recursive superquantile approximation
Manon Costa, Sébastien Gadat
In this work, we study a new recursive stochastic algorithm for the joint estimation of quantile and superquantile of an unknown distribution. The novelty of this algorithm is to u…