75 citations · 93 across the 19 of their papers we have counts for
22 papers
Complexity reduction in online stochastic Newton methods with potential O(N d) total cost
Antoine Godichon-Baggioni, Bruno Portier, Guillaume Sallé
Optimizing smooth convex functions in stochastic settings, where only noisy estimates of gradients and Hessians are available, is a fundamental problem in optimization. While first…
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…
Theoretical Convergence Guarantees for Variational Autoencoders
Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff
Variational Autoencoders (VAE) are popular generative models used to sample from complex data distributions. Despite their empirical success in various machine learning tasks, sign…
Semi-Discrete Optimal Transport: Nearly Minimax Estimation With Stochastic Gradient Descent and Adaptive Entropic Regularization
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
Optimal Transport (OT) based distances are powerful tools for machine learning to compare probability measures and manipulate them using OT maps. In this field, a setting of intere…
A Full Adagrad algorithm with O(Nd) operations
Antoine Godichon-Baggioni, Wei Lu, Bruno Portier
A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization. By developing a recursi…
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
Sobihan Surendran, Antoine Godichon-Baggioni, Adeline Fermanian +1
Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep neural networks and generative models. Most theoretical results assume that it is possible to obt…
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
Antoine Godichon-Baggioni, Wei Lu, Bruno Portier
This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation. A direct recursive estimation technique for t…
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
Antoine Godichon-Baggioni, Nicklas Werge
Stochastic optimization methods encounter new challenges in the realm of streaming, characterized by a continuous flow of large, high-dimensional data. While first-order methods, l…
A mixture of ellipsoidal densities for 3D data modelling
Denis Brazey, Antoine Godichon-Baggioni, Bruno Portier
In this paper, we propose a new ellipsoidal mixture model. This model is based a new probability density function belonging to the family of elliptical distributions and designed t…
Online stochastic Newton methods for estimating the geometric median and applications
Antoine Godichon-Baggioni, Wei Lu
In the context of large samples, a small number of individuals might spoil basic statistical indicators like the mean. It is difficult to detect automatically these atypical indivi…
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications
Antoine Godichon-Baggioni, Pierre Tarrago
In stochastic optimization, a common tool to deal sequentially with large sample is to consider the well-known stochastic gradient algorithm. Nevertheless, since the stepsequence i…
A penalized criterion for selecting the number of clusters for K-medians
Antoine Godichon-Baggioni, Sobihan Surendran
Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for con…
Learning from time-dependent streaming data with online stochastic algorithms
Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger
This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates. We analyze several first-order methods, including Stochastic…
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger
We introduce a streaming framework for analyzing stochastic approximation/optimization problems. This streaming framework is analogous to solving optimization problems using time-v…
Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient
Antoine Godichon-Baggioni
Online averaged stochastic gradient algorithms are more and more studied since (i) they can deal quickly with large sample taking values in high dimensional spaces, (ii) they enabl…
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions
Claire Boyer, Antoine Godichon-Baggioni
The majority of machine learning methods can be regarded as the minimization of an unavailable risk function. To optimize the latter, given samples provided in a streaming fashion,…
An efficient Averaged Stochastic Gauss-Newton algorithm for estimating parameters of non linear regressions models
Peggy Cénac, Antoine Godichon-Baggioni, Bruno Portier
Non linear regression models are a standard tool for modeling real phenomena, with several applications in machine learning, ecology, econometry... Estimating the parameters of the…
On the rates of convergence of Parallelized Averaged Stochastic Gradient Algorithms
Antoine Godichon-Baggioni, Sofiane Saadane
The growing interest for high dimensional and functional data analysis led in the last decade to an important research developing a consequent amount of techniques. Parallelized al…
Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data
Antoine Godichon-Baggioni, Cathy Maugis-Rabusseau, Andrea Rau
Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e., data made up…
Online estimation of the asymptotic variance for averaged stochastic gradient algorithms
Antoine Godichon-Baggioni
Stochastic gradient algorithms are more and more studied since they can deal efficiently and online with large samples in high dimensional spaces. In this paper, we first establish…
Lp and almost sure rates of convergence of averaged stochastic gradient algorithms: locally strongly convex objective
Antoine Godichon-Baggioni
An usual problem in statistics consists in estimating the minimizer of a convex function. When we have to deal with large samples taking values in high dimensional spaces, stochast…
An averaged projected Robbins-Monro algorithm for estimating the parameters of a truncated spherical distribution
Antoine Godichon-Baggioni, Bruno Portier
The objective of this work is to propose a new algorithm to fit a sphere on a noisy 3D point cloud distributed around a complete or a truncated sphere. More precisely, we introduce…