Abstract: The denoising diffusion probabilistic model (DDPM) has become a cornerstone of generative AI. While sharp convergence guarantees have been established for DDPM, the iteration complexity typically scales with the ambient data dimension of target distributions, leading to overly conservative theory that fails to explain its practical efficiency. This has sparked recent efforts to understand how DDPM can achieve sampling speed-ups through automatic exploitation of intrinsic low dimensionality of data. This talk explores two key scenarios: (1) For a broad class of data distributions with intrinsic dimension k, we prove that the iteration complexity of the DDPM scales nearly linearly with k, which is optimal under the KL divergence metric; (2) For mixtures of Gaussian distributions with k components, we show that DDPM learns the distribution with iteration complexity that grows only logarithmically in k. These results provide theoretical justification for the practical efficiency of diffusion models.
Biography: Dr. Yuting Wei is an Associate Professor in the Statistics and Data Science Department at the Wharton School, University of Pennsylvania. Prior to that, Dr. Wei spent two years at Carnegie Mellon University as an assistant professor and one year at Stanford University as a Stein's Fellow. She received her Ph.D. in statistics at the University of California, Berkeley. She was the recipient of the 2025 Gottfried E. Noether Early Career Scholar Award, Google Research Scholar Award, NSF Career award, and the Erich L. Lehmann Citation from the Berkeley statistics department. Her research interests include high-dimensional and non-parametric statistics, reinforcement learning, and diffusion models.
