6 papers · 1 filter
Provable diffusion-based posterior sampling for linear inverse problems via DDIM
Yuchen Jiao, Na Li, Changxiao Cai +2
Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantee…
Confidence-Based Decoding is Provably Efficient for Diffusion Language Models
Changxiao Cai, Gen Li
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models for language modeling, allowing flexible generation order and parallel genera…
Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
Gen Li, Changxiao Cai
Diffusion models have emerged as a powerful paradigm for modern generative modeling, demonstrating strong potential for large language models (LLMs). Unlike conventional autoregres…
Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
Gen Li, Changxiao Cai, Yuting Wei
Diffusion models are distinguished by their exceptional generative performance, particularly in producing high-quality samples through iterative denoising. While current theory sug…
Minimax Optimality of the Probability Flow ODE for Diffusion Models
Changxiao Cai, Gen Li
Score-based diffusion models have become a foundational paradigm for modern generative modeling, demonstrating exceptional capability in generating samples from complex high-dimens…
Provable Acceleration for Diffusion Models under Minimal Assumptions
Gen Li, Changxiao Cai
Score-based diffusion models, while achieving minimax optimality for sampling, are often hampered by slow sampling speeds due to the high computational burden of score function eva…