Antoine Godichon-Baggioni | DeepAI

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Wei Lu
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Bruno Portier
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∙ 09/21/2023

A mixture of ellipsoidal densities for 3D data modelling

In this paper, we propose a new ellipsoidal mixture model. This model is...

0 Denis Brazey, et al. ∙

∙ 04/03/2023

Online stochastic Newton methods for estimating the geometric median and applications

In the context of large samples, a small number of individuals might spo...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 03/01/2023

Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications

In stochastic optimization, a common tool to deal sequentially with larg...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 11/15/2022

A robust model-based clustering based on the geometric median and the Median Covariation Matrix

Grouping observations into homogeneous groups is a recurrent task in sta...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 09/08/2022

A penalized criterion for selecting the number of clusters for K-medians

Clustering is a usual unsupervised machine learning technique for groupi...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 05/25/2022

Learning from time-dependent streaming data with online stochastic algorithms

We study stochastic algorithms in a streaming framework, trained on samp...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 09/15/2021

Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data

Motivated by the high-frequency data streams continuously generated, rea...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 07/26/2021

Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

Online averaged stochastic gradient algorithms are more and more studied...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 11/19/2020

On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions

The majority of machine learning methods can be regarded as the minimiza...

0 Claire Boyer, et al. ∙

∙ 06/23/2020

An efficient Averaged Stochastic Gauss-Newton algorithm for estimating parameters of non linear regressions models

Non linear regression models are a standard tool for modeling real pheno...

0 Peggy Cénac, et al. ∙

∙ 06/23/2020

An efficient Averaged Stochastic Gauss-Newtwon algorithm for estimating parameters of non linear regressions models

Non linear regression models are a standard tool for modeling real pheno...

0 Peggy Cénac, et al. ∙

∙ 04/16/2019

An efficient stochastic Newton algorithm for parameter estimation in logistic regressions

Logistic regression is a well-known statistical model which is commonly ...

0 Bernard Bercu, et al. ∙

∙ 10/22/2017

On the rates of convergence of Parallelized Averaged Stochastic Gradient Algorithms

The growing interest for high dimensional and functional data analysis l...

0 Antoine Godichon-Baggioni, et al. ∙

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