Jarzynski reweighting, which estimates normalization constants from out-of-equilibrium paths, is shown to apply to a broad class of sampling kernels, including drift-based stochastic interpolants and RBM Gibbs sampling, with weights that vanish in the continuous-time limit.
A survey on bias and fairness in machine learning.ACM Computing Surveys (CSUR), 54(6):1–35, 2021
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Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels
Jarzynski reweighting, which estimates normalization constants from out-of-equilibrium paths, is shown to apply to a broad class of sampling kernels, including drift-based stochastic interpolants and RBM Gibbs sampling, with weights that vanish in the continuous-time limit.