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【讲座报告】Probabilistic inference in relational models

来源:计算机科学与技术学院 Admin   发布日期: 2019-09-19 04:52:59

讲座标题:Probabilistic inference in relational models

主讲人: 王彧弋

讲座时间:2019-9-25  15:00:00

讲座地点:临港校区计电楼A201会议室

讲座语言:中文

主办单位:计算机科学与技术学院

讲座内容:

In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make the following contributions. First, we compare two different notions of relational marginals. Second, we show a duality between the resulting relational marginal problems and the maximum likelihood estimation of the parameters of relational models, which generalizes a well-known duality from the propositional setting. Third, by exploiting the relational marginal formulation, we present a statistically sound method to learn the parameters of relational models that will be applied in settings where the number of constants differs between the training and test data. Furthermore, based on a relational generalization of marginal polytopes, we characterize cases where the standard estimators based on feature's number of true groundings needs to be adjusted and we quantitatively characterize the consequences of these adjustments. Fourth, we prove bounds on expected errors of the estimated parameters, which allows us to lower-bound, among other things, the effective sample size of relational training data.

主讲人简介:

王彧弋,X-Order Lab Leader, 苏黎世联邦理工计算机科学博士后。主要研究方向:理论计算机科学(区块链理论、计算经济学等)和机器学习(偏理论算法研究)。在A+级计算机会议/期刊发表论文十余篇;在A级计算机会议/期刊发表论文3篇,具体学术成果详见个人主页:https://disco.ethz.ch/members/yuwang。目前主持X-Order Research Lab研究工作。


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