Approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations
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Abstract (arXiv)
We study in this paper a weak approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations in Wasserstein-1 distance, and obtain a uniform error bound. Our approach is via a refined Lindeberg principle and Malliavin calculus.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2022Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimizationcited by 16
- 2025Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEscited by 5
- 2022Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problemscited by 3
- 2025Approximation to Deep Q-Network by Stochastic Delay Differential Equationscited by 0
- 2025Approximation to Deep Q-Network by Stochastic Delay Differential Equations1 May 2025
- 2025Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs24 Feb 2025
- 2022Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization30 Mar 2022
- 2022Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems19 Mar 2022