5 papers
Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
Yu Yao, Huanjian Zhou, Andi Han +2
Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of…
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
Wei Huang, Andi Han, Mingyuan Bai +4
Diffusion models generate high-dimensional data with remarkable quality, yet how their training efficiently learns the score function, bypassing the curse of dimensionality when da…
Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models
Huanjian Zhou, Masashi Sugiyama
Sampling from high-dimensional probability distributions is fundamental in machine learning and statistics. As datasets grow larger, computational efficiency becomes increasingly i…
The adaptive complexity of parallelized log-concave sampling
Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama
In large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we…
The Adaptive Complexity of Finding a Stationary Point
Huanjian Zhou, Andi Han, Akiko Takeda +1
In large-scale applications, such as machine learning, it is desirable to design non-convex optimization algorithms with a high degree of parallelization. In this work, we study th…