1 citations · 1 across the 5 of their papers we have counts for
5 papers
Importance Sampling Optimization with Laplace Principle
Radu-Alexandru Dragomir, François Portier, Victor Priser
Grid search and random search are widely used techniques for hyperparameter tuning in machine learning, especially when gradient information is unavailable. In these methods, a fin…
Consensus-Based Optimization Beyond Finite-Time Analysis
Pascal Bianchi, Radu-Alexandru Dragomir, Victor Priser
We analyze a zeroth-order particle algorithm for the global optimization of a non-convex function, focusing on a variant of Consensus-Based Optimization (CBO) with small but fixed…
Stochastic mirror descent for nonparametric adaptive importance sampling
Pascal Bianchi, Bernard Delyon, Victor Priser +1
This paper addresses the problem of approximating an unknown probability distribution with density -- which can only be evaluated up to an unknown scaling factor -- with the he…
Long-time asymptotics of noisy SVGD outside the population limit
Victor Priser, Pascal Bianchi, Adil Salim
Stein Variational Gradient Descent (SVGD) is a widely used sampling algorithm that has been successfully applied in several areas of Machine Learning. SVGD operates by iteratively…
Long run convergence of discrete-time interacting particle systems of the McKean-Vlasov type
Pascal Bianchi, Walid Hachem, Victor Priser
We consider a discrete-time system of n coupled random vectors, a.k.a. interacting particles. The dynamics involve a vanishing step size, some random centered perturbations, and a…