2 papers
stat.ML2026
Adaptive Diffusion Guidance via Stochastic Optimal Control
Iskander Azangulov, Peter Potaptchik, Qinyu Li +3
Guidance is a cornerstone of modern diffusion models, playing a pivotal role in conditional generation and enhancing the quality of unconditional samples. However, current approach…
stat.ML2025
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions
Iskander Azangulov, George Deligiannidis, Judith Rousseau
Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used f…