3 papers
cs.LG2026
Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models
Xun Su, Hiroyuki Kasai
Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation information into the generation process of the d…
cs.LG2025
Navigating the Exploration-Exploitation Tradeoff in Inference-Time Scaling of Diffusion Models
Xun Su, Jianming Huang, Yang Yusen +2
Inference-time scaling has achieved remarkable success in language models, yet its adaptation to diffusion models remains underexplored. We observe that the efficacy of recent Sequ…
cs.LG2025
Anchor Space Optimal Transport as a Fast Solution to Multiple Optimal Transport Problems
Jianming Huang, Xun Su, Zhongxi Fang +1
In machine learning, Optimal Transport (OT) theory is extensively utilized to compare probability distributions across various applications, such as graph data represented by node…