2 citations · 3 across the 5 of their papers we have counts for
6 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…
Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing
Zhihan Huang, Ziang Niu
Goodness-of-fit (GoF) tests are fundamental for assessing model adequacy. Score-based tests are appealing because they require fitting the model only once under the null. However,…
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…
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…