4 papers
On the Kantorovich contraction of Markov semigroups
Pierre Del Moral, Mathieu Gerber
This paper develops a novel operator theoretic framework to study the contraction properties of Markov semigroups with respect to a general class of Kantorovich semi-distances, whi…
Safety of particle filters: Some results on the time evolution of particle filter estimates
Mathieu Gerber
Particle filters (PFs) form a class of Monte Carlo algorithms that propagate over time a set of particles which can be used to estimate, in an online fashion, the sequenc…
regMMD: An R package for parametric estimation and regression with maximum mean discrepancy
Pierre Alquier, Mathieu Gerber
The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for para…
Gradient Descent for Convex and Smooth Noisy Optimization
Feifei Hu, Mathieu Gerber
We study the use of gradient descent with backtracking line search (GD-BLS) to solve the noisy optimization problem $θ_\star:=\mathrm{argmin}_{θ\in\mathbb{R}^d} \mathbb{E}[f(θ,Z…