2 citations · 3 across the 7 of their papers we have counts for
7 papers
Provably adaptive sampling with uniform and remasking discrete diffusion models
Daniil Dmitriev, Zhihan Huang, Yuting Wei
Discrete diffusion models offer a promising alternative to autoregressive generation by enabling parallel updates, but their sampling efficiency can depend strongly on the choice o…
Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
Daniil Dmitriev, Zhihan Huang, Yuting Wei
Diffusion models over discrete spaces have recently shown striking empirical success, yet their theoretical foundations remain incomplete. In this paper, we study the sampling effi…
Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy
Zhihan Huang, Ziang Niu
Models with intractable normalizing constants are widely used in statistics and machine learning. Assessing the adequacy of such models poses significant challenges: obtaining samp…
Low-dimensional adaptation of diffusion models: Convergence in total variation
Jiadong Liang, Zhihan Huang, Yuxin Chen
This paper investigates how diffusion generative models leverage (unknown) low-dimensional structure to accelerate sampling. Focusing on two mainstream samplers -- the denoising di…
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality
Zhihan Huang, Yuting Wei, Yuxin Chen
The denoising diffusion probabilistic model (DDPM) has emerged as a mainstream generative model in generative AI. While sharp convergence guarantees have been established for the D…
Towards a mathematical theory for consistency training in diffusion models
Gen Li, Zhihan Huang, Yuting Wei
Consistency models, which were proposed to mitigate the high computational overhead during the sampling phase of diffusion models, facilitate single-step sampling while attaining s…
Azadkia-Chatterjee's correlation coefficient adapts to manifold data
Fang Han, Zhihan Huang
In their seminal work, Azadkia and Chatterjee (2021) initiated graph-based methods for measuring variable dependence strength. By appealing to nearest neighbor graphs, they gave an…