学　术

　　为加强我校各学科之间的学术交流，搭建教师学术交流平台，促进教师学术水平提升和跨学科合作，教师发展中心开展跨学科学术沙龙活动。

　　本次活动教师发展中心特别邀请来自澳大利亚悉尼科技大学的徐亦达博士，与我校师生分享他在机器学习领域的思考和心得。具体安排如下，欢迎感兴趣的教师和博士生参加。

　　一、主　题：Correlated samples from Dirichlet Process Marginal

　　二、主讲人：澳大利亚悉尼科技大学　徐亦达博士

　　三、时　间：10月19日（周三）上午10:30

　　四、地　点：清水河校区主楼B1-104

　　五、主持人：徐增林教授

　　六、内容简介

　　Drawing bivariate samples from two Dirichlet Processes (DPs) can be a challenging yet important problem: in a setting where we wish to leave the individual stick-breaking weights marginal invariant, adding a copula function can be a plausible approach. However, the vanilla Gibbs sampling to the model can result in slow-mixing, yet, there is no analytical solution to a collapsed Gibbs Sampling where both the stick-breaking weights and the copula variable can be integrated out simultaneously. In this talk, we provide the details (and visualisation) of a partial collapsed approach: we integrate out either the stick-breaking weights or the copula variable condition on the other. Luckily, both conditional probabilities are in their close-form. We applied this framework in Mixed-Membership Stochastic Blockmodel where intra-group correlation can be modelled through the copula functions. This paper was first published in arXiv in June 2013 and then International Joint Conference on AI (IJCAI) in June 2016.

　　七、主讲人简介

　　Yida Xu is the director of Machine Learning and Data Analytics Lab @GDBTC::UTS. He lead a group of 12 talented PhD students and engineers to apply our research and engineering skills to both Government and Retail insight analytics. In addition to cutting edge research, his group has also mastered modern data science tools, including Spark and TensorFlow.

　　Yida Xu is a world-renowned first line researcher in Machine Learning, Data Analytics, Computer Vision and Deep Learning. He wrote a series of Statistics, Probability and Machine Learning course for PhD students around the world. Research Interests are machine Learning, Data Analytics and Computer Vision.

　　八、主办单位：人力资源部教师发展中心

　　　　承办单位：计算机科学与工程学院、统计机器智能与学习实验室SMILE Lab

　　　　　　　　　　　　　　　　　　　　　　人力资源部教师发展中心

　　　　　　　　　　　　　　　　　　　　　　　2016年10月17日