arXiv Machine Learning By Benedict Leimkuhler, Peter A. Whalley

Accelerated sampling using SamAdams variable timesteps and position-adaptive Langevin dynamics

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arXiv:2606. 26881v1 Announce Type: cross Abstract: We introduce an accelerated Langevin-based sampling method that is based on two complementary devices: \emph{SamAdams} adaptive timestepping, which automatically shrinks the effective integration step in stiff regions of phase space using a relaxed stiffness monitor, and \emph{position-adaptive Langevin} (PAL) dynamics, which concentrates friction along the local force direction while preserving the canonical distribution as the exact invariant measure.

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arXiv Machine Learning
Aug 10

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs

arXiv:2601. 08527v3 Announce Type: replace-cross Abstract: We propose a novel method for sampling from unnormalized Boltzmann densities based on a probability flow ordinary differential equation (ODE) derived from linear stochastic interpolants.

By Chenguang Duan, Yuling Jiao, Gabriele Steidl, Christian Wald, Jerry Zhijian Yang, Ruizhe Zhang