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Found 5 papers, 1 papers with code
Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations".
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
We exhibit similar bounds using both approximate and stochastic gradients, and our method's computational cost is shown to scale logarithmically with the size of the dataset.
On Mixing Times of Metropolized Algorithm With Optimization Step (MAO) : A New Framework
In this paper, we consider sampling from a class of distributions with thin tails supported on $\mathbb{R}^d$ and make two primary contributions.
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Performing exact Bayesian inference for complex models is computationally intractable.
Hamiltonian Descent Methods
Yet, crucially the kinetic gradient map can be designed to incorporate information about the convex conjugate in a fashion that allows for linear convergence on convex functions that may be non-smooth or non-strongly convex.
