First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
Implicit score matching meets denoising score matching: improved rates of convergence and log-density Hessian estima- tion.arXiv preprint arXiv:2512.24378,
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Diffusion-based denoising score matching avoids the mode-separation degradation that affects vanilla score matching error bounds, via suitable hyperparameter choice.
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Finite Sample Bounds for Learning with Score Matching
First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
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Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
Diffusion-based denoising score matching avoids the mode-separation degradation that affects vanilla score matching error bounds, via suitable hyperparameter choice.