New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.
Error analysis of randomized Runge–Kutta methods for differential equations with time-irregular coefficients.Computational Methods in Applied Mathematics, 17(3):479–498, 2017
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
When Langevin Monte Carlo Meets Randomization: New Sampling Algorithms with Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness
New RSLMC sampling algorithms achieve uniform-in-time W2 error bounds of order O(sqrt(d) h) under gradient Lipschitz and log-Sobolev assumptions, with modified versions for superlinear gradient growth and supporting numerical examples.