12 citations · 15 across the 5 of their papers we have counts for
5 papers
Convergence of Multi-Level Markov Chain Monte Carlo Adaptive Stochastic Gradient Algorithms
Antoine Godichon-Baggioni, Gabriel Lang, Sylvain Le Corff +2
Stochastic optimization in learning and inference often relies on Markov chain Monte Carlo (MCMC) to approximate gradients when exact computation is intractable. However, finite-ti…
Phantom distribution functions for some stationary sequences
Paul Doukhan, Adam Jakubowski, Gabriel Lang
The notion of a phantom distribution function (phdf) was introduced by O'Brien (1987). We show that the existence of a phdf is a quite common phenomenon for stationary weakly depen…
Evaluation for moments of a ratio with application to regression estimation
Paul Doukhan, Gabriel Lang
Ratios of random variables often appear in probability and statistical applications. We aim to approximate the moments of such ratios under several dependence assumptions. Extendin…
Dependent Lindeberg central limit theorem and some applications
Jean-Marc Bardet, Paul Doukhan, Gabriel Lang +1
In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. The…
A functional central limit theorem for interacting particle systems on transitive graphs
Paul Doukhan, Gabriel Lang, Sana Louhichi +1
A finite range interacting particle system on a transitive graph is considered. Assuming that the dynamics and the initial measure are invariant, the normalized empirical distribut…