報告承辦單位: 數學與統計學院
報告內容:
?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 in which the gradient can be obtained in a much more computationally feasible manner. More specifically, we approximate the forward model using a deep neural network (DNN) which is trained on a carefully chosen training set, which also determines the quality of the surrogate. 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.
報告人姓名: 閆亮
報告人所在單位: 東南大學數學學院
報告人職稱/職務及學術頭銜: 副教授
報告時間: 2021年12月04日11:40－12:20
報告地點: 卡斯迪漫享酒店二樓VIP會議廳
報告人簡介: 閆亮，副教授、博士生導師，2011年畢業于蘭州大學數學與統計學院。主要從事不確定性量化、貝葉斯反問題理論與算法的研究。2018年入選東南大學“至善青年學者”（A層次）支持計劃，2017年入選江蘇省高校“青藍工程”優秀青年骨干教師培養對象。目前主持國家自然科學基金面上項目一項，主持完成國家自然科學基金青年項目和江蘇省自然科學基金青年項目各一項。已經在《SIAM J. Sci. Comput.》、《Inverse Problems》、《J. Comput. Phys.》等國內外刊物上發表20多篇學術論文.