报告题目1：Energy-based adaptive CUR matrix decomposition

报告时间：12月22日下午15:00

报告地点：科学会堂A710

报告摘要：

CUR decompositions are interpretable data analysis tools that express a data matrix in terms of a small number of actual columns and/or actual rows of the data matrix. One bottleneck of existing relative-error CUR algorithms lies on high computational complexity for computing important sampling probabilities. In this talk, we provide a simple yet effective framework that considers energy-based sampling algorithm. On one hand, we provide a intuitive and fast relative-error sampling algorithm for column selection problem. On the other hand, by combining the relative-error sampling algorithm with adaptive sampling algorithm we provide a novel CUR matrix approximation algorithms which is referred to as energy based adaptive sampling algorithm. The sampling algorithm is the first adaptive relative-error CUR decomposition in the coherent sense. Our empirical results exactly indicate that the new adaptive sampling algorithm typically achieves a good balance between computational complexity and approximate accuracy.

报告题目2：An Introduction to Statistical Learning

报告时间：12月23日下午15:00

报告地点：科学会堂A710

报告摘要：

Statistical learning refers to a vast set of tools for understanding data. These tools can be classified as supervised or unsupervised. In general, supervised statistical learning involves building a statistical model for predicting, or estimating, an output based on one or more inputs. First of all, we leads to the problems studied by statistical learning through several examples. Second, we describes why the unknown function f should be estimated from the perspective of prediction and inference. Broadly speaking, most statistical learning methods for this task can be characterized as either parametric or non-parametric. Finally, We detailed discuss these parametric non-parametric two types of approaches to estimate the unknown function f.

报告人简介：

徐礼文，博士，教授。2006年博士毕业于北京工业大学概率论与数理统计专业，2006-2008在清华大学数学科学系做博士后研究，2008年至今在北方工业大学统计学系工作。获得2010年北京市博士后杰出英才奖；入选2014年北京市属高等学校青年拔尖人才培育计划；2017年起担任中国现场统计研究会理事；2018年起担任全国工业统计学教学研究会理事；2017年8月至2018年8月在美国佐治亚大学统计系/大数据分析实验室访问一年。

现主要从事Ensemble Learning，Data Fusion，Decentralized Algorithm，Smoothing Spline和Subsampling Approach for Large-Scale Data等领域的研究。在国内外学术期刊上发表论文40余篇，出版专著2部；主持国家和省部级项目5项；获得北京市教育教学成果奖一等奖1项。