学者论坛: 统计机器学习

文:教师发展中心 / 来源:党委教师工作部、人力资源部 / 2016-06-14 / 点击量:3694

  人力资源部教师发展中心开展“学者论坛”系列学术活动,定期邀请与我校教师研究领域相关的知名学者,以专题报告的形式与师生分享和研讨最新研究动态和成果,旨在为教师提供学术探讨和交流的平台,开拓教师学术视野。现将第24期论坛安排如下,欢迎广大师生参加。

  一、时 间:2016年6月16日(周四)上午9:30

  二、地 点:清水河校区主楼B1-104

  三、论坛安排

  1、报告一:

  (1)主题:Structure Learning for High Dimensional Partially Varying-Coefficient Model

  (2)时间:西南财经大学 吕绍高副教授、博导

  (3)内容简介:

  Partially varying coefficient models (PVCM) provide a useful class of tools for modelling complex data by incorporating a combination of constant and time-varying covariate effects. One natural question is that how to decide which covariates correspond to constant coefficients and which correspond to time-dependent coefficient functions. The structure selection problem is fundamentally important, since tackling this problem enhances model interpretation and avoids over fitting, as well as keeps the model flexibility. To address this issue, this paper proposes a new approach to estimation and structure selection for PVCM. Within a high-dimensional framework, we derive convergence rates for the prediction risk of the proposed method when each unknown time-dependent coefficient lies in a reproducing kernel Hilbert space. Our upper bounds in $\|\cdot\|_2$ and $\|\cdot\|_n$ norms are established under two different kinds of settings, and are shown to be the optimality of our method under their individual settings. Under certain regularity conditions, we also show that the proposed estimator is able to identify the underlying structure correctly with high probability. 

  (4)主讲人简介:

  吕绍高副教授,2011年毕业于中国科大-香港城大联合培养博士项目,获得理学博士。现为西南财大统计学院副教授,博士生导师。主要研究兴趣:统计机器学习与数据挖掘,高维统计与网络模型的推断。在《journal of machine learning research》《neural computation》《Annals of institution of Statistical mathematics》等计算机或统计类国际杂志发表论文10余篇。

  2、报告二:

  (1)主题:Learning Dynamical Systems

  (2)主讲人:比利时鲁汶大学 冯云龙博士

  (3)内容简介:

  In this presentation, I will report our recent work on some learning problems in the dynamical system context. We consider a family of measure-preserving and ergodic dynamical systems. Several typical examples of the measure-preserving and ergodic dynamical systems include Gauss map, Logistic map, and beta map. Here, we are interested in estimating the chaotic maps in dynamical systems. This is done by applying the classic kernel smoothing technique from nonparametric statistics. On the other hand, by assuming that the considered dynamical system admits a unique underlying invariant density function, we are also concerned with the estimation problem of this density. The purpose is achieved by adopting the Parzen-Rosenblatt estimator. Our main results are the consistency and convergence rates of the considered estimators which are derived by employing capacity-dependent arguments and concentration inequalities developed recently in the literature. 

  (4)主讲人简介:

  Yunlong Feng received his Ph.D. degree in mathematics from the University of Science and Technology of China and a joint Ph.D. degree from the City University of Hong Kong in 2012. Currently, he is a postdoc researcher in the Department of Electrical Engineering, KU Leuven. His research interests include theory and methodologies in machine learning, with the current emphasis on robust learning and nonparametric learning in dynamical systems.

  四、主办单位:人力资源部教师发展中心

  五、承办单位:计算机科学与工程学院、大数据中心


                人力资源部教师发展中心

                   2016年6月16日


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