arXiv Machine Learning
DDPM Score Matching and Distribution Learning
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Generalized Score Matching for Parameter Estimation on Convex Domains
The paper introduces a generalized score matching objective for parameter estimation on convex subsets of ρ^d, derived from Minimum Probability Flow learning. It shows that this objective is a proper local scoring rule of second order, ensuring recovery of the true density when minimized, and proves convexity and consistency for exponential family models under standard conditions. Experiments demonstrate the method’s effectiveness on constrained domains where the partition function is intractable, including a generative modeling use‑case.
arXiv Machine Learning
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Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence
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Heteroscedasticity of Denoising Score Matching with Generalised Smooth Noise
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arXiv Machine Learning
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Diffusion models recover accurate mixture weights despite score function insensitivity
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arXiv Machine Learning
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Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification
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