Author

Urbain Vaes

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Preprint Oct 2026

Analysis of a zeroth-order multiscale sampler: strong convergence, convergence of ergodic averages and weak convergence

In many practical applications of Bayesian inference, it is difficult or undesirable to compute the gradient of the negative log-posterior, motivating the study of derivative-free samplers, also called zeroth-order samplers. The aim of this paper is to generalize and rigorously analyze a multiscale derivative-free samp...

A. Della Noce, U. Vaes · 0 citations

#machine learning Preprint Oct 2026

Measure Neural Mappings

We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operators on probability measures. Ensemble consistency requires outputs from growing ensembles sampled from an input law to converge to the specified population output in prob...

Ricardo Baptista, E. Calvello, Bo-Han Chen et al. · 0 citations

Preprint Aug 2026

A spectral approach to the narrow escape problem in two-dimensional domains

We study the law of the exit time and exit point of a Brownian motion in a two-dimensional domain with reflecting boundary conditions, except on small disjoint exit windows through which the stochastic process can escape the domain. In the limit of infinitely small exit windows, it is natural to assume that the process...

L. Carillo, T. Lelièvre, Thomas Normand et al. · 2 citations