报告题目: Sampling and generative learning

报告摘要: Estimating underlying distributions from data and sampling from unnormalized densities are two fundamental tasks. In this talk we will present a framework with theoretical guarantees to achieve these goals via bring the strength of mechanism (optimal transportation, gradient flow on measure spaces, ODE, SDE) and data (deep neural networks fitting). Meanwhile, I will talk about a global optimization algorithm via sampling.

报告时间：2024年1月15日下午3:00-5:30

报告地点：腾讯会议 147 786 634

个人简介：焦雨领，武汉大学数学统计学院副教授、博导，入选国家高层次人才青年学者计划。主要从事机器学习、科学计算的研究。现任ACM Transaction on Probabilistic Machine Learning 编委，中国现场统计学会机器学习分会副理事长。相关工作发表在包括Ann. Stat.、J. Amer. Statist. Assoc.、Statist. Sci.、 SIAM J. Math. Anal.、SIAM J. Control Optim.、 SIAM J. Numer. Anal.、SIAM J. Sci. Comput.、Appl. Comput. Harmon. Anal.、Inverse Probl.、 IEEE Trans. Inf. Theory、IEEE Trans. Signal Process.、J. Mach. Learn. Res.、ICML、NeurIPS 等期刊和会议上。