Title: Efficient Sampling with Diffusion Models: Sharp and Adaptive Guarantees
Seminar Speaker: Yuting Wei, Associate Professor, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania.
The score-based diffusion models have become a cornerstone of modern generative AI. While recent works aim to develop sharp convergence guarantees, the iteration complexity of existing analyses typically scales with the ambient data dimension $d$ of the target distribution, leading to overly conservative theory that fails to explain its practical efficiency. This motivates us to understand how diffusion models can achieve sampling speed-ups through automatic exploitation of intrinsic low dimensionality of data for both continuous and discrete distributions.
This talk explores two key scenarios: (1) For a broad class of continous distributions with intrinsic dimension $k$, we show that the iteration complexity of the denoising diffusion probabilistic model (DDPM) scales nearly linearly with $k$, which is optimal under the KL divergence metric; (2) For masking discrete diffusions, under a continuous-time Markov chain (CTMC) formulation, we introduce a modified $\tau$-leaping sampler whose convergence rate is governed by an intrinsic information-theoretic quantity, termed the \emph{effective total correlation}, which is upper bounded by $d \log S$ (with $S$ the vocabulary size) but can be sublinear or even constant for structured discrete distributions.
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 2026 Peter Gavin Hall IMS Early Career Prize, 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.