Sharp Lipschitz regularity for flow-matching vector fields and diffusion scores, with optimal time/dimension dependence, gives √d/N Wasserstein discretization error for Euler samplers and globally Lipschitz Gaussian-to-target transport maps implying Poincaré and log-Sobolev inequalities.
Wasserstein bounds for generative diffusion models with gaussian tail targets
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CDLF applies conditional diffusion models to produce probabilistic life-cycle forecasts for new products by conditioning on static descriptors and reference trajectories from similar items.
The paper proves Hölder continuity of optimal transport maps for PDE-induced measures via doubling conditions and derives excess-risk bounds for one-step generative models like DeepParticle.
citing papers explorer
-
Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities
Sharp Lipschitz regularity for flow-matching vector fields and diffusion scores, with optimal time/dimension dependence, gives √d/N Wasserstein discretization error for Euler samplers and globally Lipschitz Gaussian-to-target transport maps implying Poincaré and log-Sobolev inequalities.
-
Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
CDLF applies conditional diffusion models to produce probabilistic life-cycle forecasts for new products by conditioning on static descriptors and reference trajectories from similar items.
-
On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures
The paper proves Hölder continuity of optimal transport maps for PDE-induced measures via doubling conditions and derives excess-risk bounds for one-step generative models like DeepParticle.