4 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…
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…
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…
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…