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Cited article:
Antoine Godichon-Baggioni
ESAIM: PS, 23 (2019) 841-873
Published online: 2019-12-24
This article has been cited by the following article(s):
6 articles
Non-asymptotic analysis of Stochastic approximation algorithms for streaming data
Antoine Godichon-Baggioni, Nicklas Werge and Olivier Wintenberger ESAIM: Probability and Statistics 27 482 (2023) https://doi.org/10.1051/ps/2023006
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions
Claire Boyer and Antoine Godichon-Baggioni Computational Optimization and Applications 84 (3) 921 (2023) https://doi.org/10.1007/s10589-022-00442-3
Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient
Antoine Godichon-Baggioni Statistics 57 (3) 637 (2023) https://doi.org/10.1080/02331888.2023.2213371
Optimal non-asymptotic analysis of the Ruppert–Polyak averaging stochastic algorithm
Sébastien Gadat and Fabien Panloup Stochastic Processes and their Applications 156 312 (2023) https://doi.org/10.1016/j.spa.2022.11.012
Asymptotic distribution and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures
Bernard Bercu and Jérémie Bigot The Annals of Statistics 49 (2) (2021) https://doi.org/10.1214/20-AOS1987
An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions
Bernard Bercu, Antoine Godichon and Bruno Portier SIAM Journal on Control and Optimization 58 (1) 348 (2020) https://doi.org/10.1137/19M1261717