mlsanityData to 14 Oct 2026

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

arXiv 2402.08711 ↗First posted 13 Feb 2024stat.ML cs.LG cs.NAcited by 2

Not on the map. Why?

Abstract (arXiv)

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". They analyze the UBU integrator which is strong order two and only requires one gradient evaluation per step, resulting in desirable non-asymptotic guarantees, in particular O(d1/4ε-1/2) steps to reach a distance of ε > 0 in Wasserstein-2 distance away from the target distribution. However, there is a mistake in the local error estimates in Sanz-Serna and Zygalakis (2021), in particular, a stronger assumption is needed to achieve these complexity estimates. This note reconciles the theory with the dimension dependence observed in practice in many applications of interest.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

3 of 4 references.

References · 3

  1. 2023When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?cited by 15
  2. 2023Unbiased Kinetic Langevin Monte Carlo with Inexact Gradientscited by 10
  3. 2021Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equationscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.