5 papers
A perturbation theory for Feynman-Kac models and their particle interpretations
Pierre Del Moral, Mathieu Gerber
We develop a perturbation analysis of Feynman--Kac models, describing how local errors introduced at each step of the Feynman--Kac recursion propagate over time. Under general cond…
On the Kantorovich contraction of Markov semigroups
Pierre Del Moral, Mathieu Gerber
We develop an operator-theoretic framework for the contraction properties of Markov semigroups with respect to a general class of Kantorovich semi-distances, which includes Wassers…
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…
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…
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 …