5 papers
Finding Stationary Points under Higher-Order Smoothness: Trapping Algorithms and First-Order Lower Bounds
Huanjian Zhou, Masashi Sugiyama, Taiji Suzuki
We study the query complexity of finding approximate stationary points under higher-order smoothness using only function values or only gradients. Our results cover different dimen…
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…
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…
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…