Author

Xu-Da Ye

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Preprint Oct 2026

Stochastic Gradient Leimkuhler-Matthews Integrator with Second Order Discretization Error

Stochastic gradient Langevin dynamics (SGLD) has a discretization error of first order in the step size $h$, even with the full gradient. We study the stochastic gradient Leimkuhler-Matthews (SGLM) integrator, which has the same cost as SGLD, one stochastic gradient per step, and reuses each Gaussian vector in consecut...

Xu-Da Ye, Yu-Liang Wang, Guang Lin et al. · 0 citations

Preprint Sep 2026

Mean Square Error Analysis of Stochastic Runge-Kutta Integrators

We analyze the mean square error of stochastic Runge-Kutta integrators for overdamped Langevin dynamics whose potential is strongly convex outside a bounded region. A decomposition splits the local error into a mean-zero term and a smaller remainder, and the discrete Poisson equation turns their moments into a bound on...

Xu-Da Ye · 0 citations

#machine learning Preprint Sep 2026

Mode Coverage in Normalizing Flow Boltzmann Generators via Log-Ratio Variation

The log-ratio variation $\X_\omega$, the mean absolute pairwise difference of the target-to-pushforward log-density ratio under a weighting measure under a weighting measure, is introduced and used to define KLXX, a new loss function and the Fisher--Rao gradient flow of KLXX is derived, where both variations contribute...

Qi-Han Feng, Rong-Jie Lai, Di Qi et al. · 0 citations