Abstract Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based
deterministic sampling algorithm. Despite its wide usage, understanding the theoretical …

Motivated by the challenge of sampling Gibbs measures with nonconvex potentials, we
study a continuum birth–death dynamics. We improve results in previous works (Liu et al …

We introduce $\textit {Stein transport} $, a novel methodology for Bayesian inference
designed to efficiently push an ensemble of particles along a predefined curve of tempered …

In this paper, we study efficient approximate sampling for probability distributions known up
to normalization constants. We specifically focus on a problem class arising in Bayesian …

The purpose of this paper is to answer a few open questions in the interface of kernel
methods and PDE gradient flows. Motivated by recent advances in machine learning …

We introduce a new mean-field ODE and corresponding interacting particle systems for
sampling from an unnormalized target density or Bayesian posterior. The interacting particle …

We deal with the task of sampling from an unnormalized Boltzmann density $\rho_D $ by
learning a Boltzmann curve given by energies $ f_t $ starting in a simple density $\rho_Z …

Kalman filters constitute a scalable and robust methodology for approximate Bayesian
inference, matching first and second order moments of the target posterior. To improve the …

There has been recently a lot of interest in the analysis of the Stein gradient descent method,
a deterministic sampling algorithm. It is based on a particle system moving along the …

Most inverse problems from physical sciences are formulated as PDE-constrained
optimization problems. This involves identifying unknown parameters in equations by …