时 间：2026年4月9日（周四）11：00 - 12：00
地 点：普陀校区理科大楼A1514室
报告人：焦雨领 武汉大学教授
主持人：於州 华东师范大学教授
摘 要:
Inference-time alignment for diffusion models aims to adapt a pre-trained diffusion model toward a target distribution without retraining the base score network, thereby preserving the generative capacity of the base model while enforcing desired properties at the inference time. A central mechanism for achieving such alignment is guidance, which modifies the sampling dynamics through an additional drift term. In this work, we introduce Doob's matching, a novel framework for guidance estimation grounded in Doob's -transform. Our approach formulates guidance as the gradient of logarithm of an underlying Doob's -function and employs gradient-penalized regression to simultaneously estimate both the -function and its gradient, resulting in a consistent estimator of the guidance. Theoretically, we establish non-asymptotic convergence rates for the estimated guidance. Moreover, we analyze the resulting controllable diffusion processes and prove non-asymptotic convergence guarantees for the generated distributions in the 2-Wasserstein distance.
报告人简介：
焦雨领，武汉大学人工智能学院教授、博士生导师、副院长。主要从事机器学习、反问题与科学计算研究，聚焦深度学习理论、生成式学习与表示学习。入选国家高层次青年人才计划，在包括SIAM系列期刊、Annals of Statistics、Journal of the American Statistical Association 、Journal of Machine Learning Research、IEEE Transactions on Information Theory、ICML、NeurIPS等顶级期刊与会议上发表论文数十篇。兼任中国现场统计学会机器学习分会副理事长等学术职务。