A statistical theory for blind denoising: minimax estimation of the noise level from a single sample

arXiv:2609.33008 · math.ST, stat.ML, stat.TH · Submitted 2026-09-26 · Read on arXiv

math.ST, stat.ML, stat.TH

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Diffusion Model — 15× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Ambient Dimension — 6× in this paper · explained in 1 episode
• Generative Modeling — 5× in this paper · explained in 3 episodes
• Intrinsic Dimension — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 3 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Inverse Problem — 2× in this paper · explained in 2 episodes
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Blind denoising diffusion models and the blessings of dimensionality
• Beckmann Transport Models: From Autonomous Flows to One-Step Maps

Related papers

• Conformal Prediction for Dyadic Regression Under Complex Missingness
• Bentkus-type asymptotic e-values
• High-Dimensional Asymptotics of Differentially Private PCA
• KL Convergence Guarantees for Score diffusion models under minimal data assumptions
• Geometric bias in eigenspace perturbation under random heterogeneous noise
• On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models