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Yue Wu

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Preprint Sep 2026

On the computational cost of Stochastic Gradient Langevin Dynamics

Stochastic Gradient Langevin Dynamics (SGLD) reduces the cost of Langevin-based sampling by replacing full-dataset drift evaluations with mini-batch approximations, but the resulting subsampling error may offset this computational saving. We study this trade-off for stochastic differential equations with finite-sum dri...

Mateusz B. Majka, Tigran Nagapetyan, Łukasz Szpruch et al. · 0 citations

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