It is a fundamental problem to understand the complexity of high-accuracy sampling from a
strongly log-concave density π on ℝ d. Indeed, in practice, high-accuracy samplers such as …

Langevin diffusions are rapidly convergent under appropriate functional inequality
assumptions. Hence, it is natural to expect that with additional smoothness conditions to …

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or
equivalently, the complexity of minimizing a functional over the space of probability …

We provide a nonasymptotic analysis of the convergence of the stochastic gradient
Hamiltonian Monte Carlo (SGHMC) to a target measure in Wasserstein-2 distance without …

We provide a framework to analyze the convergence of discretized kinetic Langevin
dynamics for-Lipschitz,-convex potentials. Our approach gives convergence rates of, with …

The efficacy of modern generative models is commonly contingent upon the precision of
score estimation along the diffusion path, with a focus on diffusion models and their ability to …

This paper focuses on the high-dimensional sampling of log-concave distributions with
composite structures: $ p^*(\mathrm {d} x)\propto\exp (-g (x)-f (x))\mathrm {d} x $. We …

We consider the sampling problem from a composite distribution whose potential (negative
log density) is $\sum_ {i= 1}^ n f_i (x_i)+\sum_ {j= 1}^ m g_j (y_j)+\sum_ {i= 1}^ n\sum_ {j …

This paper provides a convergence analysis for generalized Hamiltonian Monte Carlo
samplers, a family of Markov Chain Monte Carlo methods based on leapfrog integration of …

In previous work, we introduced a method for determining convergence rates for integration
methods for the kinetic Langevin equation for M-∇ Lipschitz mlog-concave densities [arXiv …