Establishes dimension- and step-optimal Wasserstein bounds for DDPMs under Lipschitz score conditions and broad variance schedules via Föllmer process analysis, recovering prior results and extending to log-concave targets.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ML 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Discretized Föllmer processes supply hyper-parameter settings for DDPM samplers that recover state-of-the-art sampling error bounds with slight improvements.
citing papers explorer
-
Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process
Establishes dimension- and step-optimal Wasserstein bounds for DDPMs under Lipschitz score conditions and broad variance schedules via Föllmer process analysis, recovering prior results and extending to log-concave targets.
-
A note on connections between the F\"ollmer process and the denoising diffusion probabilistic model
Discretized Föllmer processes supply hyper-parameter settings for DDPM samplers that recover state-of-the-art sampling error bounds with slight improvements.