We introduce a new probabilistic approach to quantify convergence to equilibrium for
(kinetic) Langevin processes. In contrast to previous analytic approaches that focus on the …

We consider contractivity for diffusion semigroups wrt Kantorovich (L^ 1 L 1 Wasserstein)
distances based on appropriately chosen concave functions. These distances are …

We consider ${\mathbb {R}}^ d $-valued diffusion processes of type\begin {align*} dX_t\=\b
(X_t) dt+ dB_t.\end {align*} Assuming a geometric drift condition, we establish contractions of …

We study a Markov process with two components: the first component evolves according to
one of finitely many underlying Markovian dynamics, with a choice of dynamics that changes …

Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the
most popular and flexible tools for sampling high-dimensional probability measures. Non …

We describe conditions on non-gradient drift diffusion Fokker–Planck equations for its
solutions to converge to equilibrium with a uniform exponential rate in Wasserstein distance …

Based on a coupling approach, we prove uniform in time propagation of chaos for weakly
interacting mean-field particle systems with possibly non-convex confinement and …

Quantitative long-time entropic convergence and short-time regularization are established
for an idealized Hamiltonian Monte Carlo chain which alternatively follows an Hamiltonian …

In this article, we study the mean field limit of weakly interacting diffusions for confining and
interaction potentials that are not necessarily convex. We explore the relationship between …

We prove quantitative convergence rates at which discrete Langevin-like processes
converge to the invariant distribution of a related stochastic differential equation. We study …