Keynote Speaker:Yan Liang

Abstract:

Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develop efficient techniques to approximate, or draw samples from posterior distributions. Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for this issue. However, the vanilla SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable or too expensive to evaluate. In this talk we explore one way to address this challenge by the construction of a local surrogate for the target distribution which the gradient can be obtained in a much more computationally feasible manner. To this end we propose a general adaptation procedure to refine the local approximation online without destroying the convergence of the resulting SVGD. This significantly reduces the computational cost of SVGD and leads to a suite of algorithms that are straightforward to implement. The new algorithm is illustrated on a set of challenging Bayesian inverse problems, and numerical experiments demonstrate a clear improvement in performance and applicability of standard SVGD.

Speaker Introduction:

Yan Liang, Associate Professor and Doctoral Supervisor of Southeast University, is mainly engaged in the research on uncertainty quantification, and theory and algorithm of the Bayesian inverse problem. In 2018, he was selected into the support plan of "Top Distinguished Young Scholar" (Level A) of Southeast University, and in 2017, he was rated as one of the training objects of outstanding youth backbone teachers of "Qing Lan Project" in Jiangsu Universities. At present, he has presided over one general project and one youth project of the National Natural Science Foundation of China, as well as one youth project of the National Natural Science Foundation of Jiangsu Province, China. He has published more than 20 academic papers inSIAM J. Sci. Comput.,Inverse ProblemsandJ. Comput. Phys..

Inviter:

Zhao Weidong Professor of School of Mathematics

Time:

11:00-12:00 on December 22 (Tuesday)

Location:

Tencent Meeting, ID: 585 798 369

https://meeting.tencent.com/s/HMGBsrd6HrFT

Hosted by: School of Mathematics, Shandong University