[1507.05021] Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status
[1507.05021] Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Mathematics > Statistics Theory arXiv:1507.05021 (math) [Submitted on 17 Jul 2015 ( v1 ), last revised 19 Dec 2016 (this version, v3)] Title: Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Authors: Alain Durmus (LTCI), Eric Moulines (CMAP) View a PDF of the paper titled Non-asymptotic convergence analysis for the Unadjusted Langevin Algorith
related reading
- [2404.19756] KAN: Kolmogorov-Arnold Networksarxiv.org
- [2503.11615v2] From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Settingarxiv.org
- [2411.13462] Sampling and Integration of Logconcave Functions by Algorithmic Diffusionarxiv.org
- 0907.4178arxiv.org
- [2505.02621] Mirror Mean-Field Langevin Dynamicsarxiv.org
- Dynamical Approach to RMT.pdfpeople.math.harvard.edu
- [2405.15441] Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distancesarxiv.org
- [2506.03719] On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticityarxiv.org
- [2506.05707] A cautious user's guide in applying HMMs to physical systemsarxiv.org
- Lightweight Asynchronous Snapshots for Distributed Dataflowsarxiv.org
- [2506.09501] Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inferencearxiv.org
- [2406.02061] Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Modelsarxiv.org