学者论坛:Quantile Regression Under Memory Constraint

文:人力资源部教师发展中心 / 来源:数学学院 党委教师工作部、人力资源部 / 2019-07-25 / 点击量:2635

  人力资源部教师发展中心“学者论坛”活动邀请上海交通大学刘卫东教授到校交流。具体安排如下,欢迎广大师生参加:

  一、主 题:Quantile Regression Under Memory Constraint

  二、时 间:2019年7月26日(星期五)11:00

  三、地 点:清水河校区 主楼A1-513

  四、主讲人:上海交通大学   刘卫东 教授  

  五、主持人:数学科学学院   孔婀芳 教授

  六、内容简介:

  This paper studies the inference problem in quantile regression (QR) for a large sample size n but under a limited memory constraint, where the memory can only store a small batch of data of size m. A natural method is the naive divide-and-conquer approach, which splits data into batches of size m, computes the local QR estimator for each batch, and then aggregates the estimators via averaging. However, this  method only works when n=o(m^2) and is computationally expensive. This paper proposes a computationally efficient method, which only requires an initial QR estimator on a small batch of data and then successively refines the estimator via multiple rounds of aggregations. Theoretically,  as long as n grows polynomially in m, we establish the asymptotic normality for the obtained estimator and show that our estimator with only a few rounds of aggregations achieves the same efficiency as the QR estimator computed on all the data. Moreover, our result allows the case that the dimensionality p goes to infinity. The proposed method can also be applied to address the QR problem under distributed computing environment (e.g., in a large-scale sensor network) or for real-time streaming data.

  七、主讲人简介:

  刘卫东,上海交通大学教授博士生导师。2003年本科毕业于浙江大学数学系,2008年获得浙江大学博士学位,2008-2011年在香港科技大学和美国宾夕法尼亚大学沃顿商学院担任博士后研究员,2018年获国家杰出青年科学基金。在统计学四大顶级期刊 AOS, JASA, JRSSB, Biometrika和概率论顶级期刊 AOP, PTRF等发表40余篇论文,主要研究方向为统计学理论和机器学习等。

  八、主办单位:人力资源部教师发展中心

    承办单位:数学科学学院

 

                   人力资源部教师发展中心

                      2019年7月25日

 

 

编辑:王晓刚  / 审核:王晓刚  / 发布者:陈伟