Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms
We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to the small-stepsize central limit theorem and under an unbiased stochastic gradient oracle, we prove that the empirical average over a horizon of order the inverse squared stepsize satisfies a central limit theorem in the vanishing-stepsize regime. The limiting variance is characterized through the Poisson equation of the limiting full-gradient diffusion. We then rewrite this constant in an operator form that links it to the continuous-time asymptotic variance and, under standard operator-theoretic assumptions, de...
| Authors | Bingye Ni, Xiaoyu Wang, Yingli Wang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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