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Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking

arXiv:2610.04568v1 Announce Type: new Abstract: We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift term that would normally contain the score $\nabla_\mathbf{x} \log p_\theta(\bf{x})$ with a high-fre…

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  1. 2026-10-06 04:00 · arXiv stat.ML
    Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking

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