主题报告专场（一）| 首届机器学习与统计会议

摘要：Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However, the original definition of HUM involves multiple integration and thus a medical investigation for multi-class ROC analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this paper. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

简介：栗家量，新加坡国立大学统计与应用概率系教授，同时在杜克大学-新加坡国大医学院与新加坡眼科研究所任兼职教授。栗教授于2001年在中国科学技术大学获得统计学学士学位，分别于2005年和2006年在美国威斯康星大学麦迪逊分校获得公共健康学硕士学位和统计学博士学位。现在研究兴趣包括工具变量、子集分析、变点模型、结构方程、精准医学、诊断医学、 模型平均、非参、生存分析等。在包括AOS、JASA、JRSSB、JOE在内的统计学期刊上发表论文160余篇，他是ASA的Fellow和ISI的Elected Member。

摘要：The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology.

简介：王小舟，华东师范大学统计学院助理教授，2015年于上海交通大学致远学院获得本科学位，2020年于上海交通大学数学科学学院获得博士学位。主要研究方向为分布式统计推断、机器学习算法、贝叶斯决策过程和交叉研究，在机器学习顶级期刊JMLR、统计学重要期刊JSCS等、人工智能顶级会议IJCAI上发表成果。主持国家自然科学基金青年科学基金项目，入选上海市“晨光计划”、上海市“扬帆计划”。

摘要：Kolmogorov-Smirnov (KS) statistic has been widely used in many areas to evaluate the performance of binary classification. However, almost no classification algorithm tries to optimize it directly at the training stage due to the computational and theoretical challenges brought by the special form of KS. In this talk, we will introduce our work about Kolmogorov-Smirnov learning which uses KS as the target function. Several methods will be discussed and their theoretical and empirical results will be presented.

简介：方方，华东师范大学统计学院教授，博士生导师。本科和博士先后毕业于北京大学数学系和美国威斯康星大学统计系。在2013年加入华东师范大学之前，曾在通用电气金融集团和上海浦东发展银行任职多年。主要研究方向为模型平均、碎片化数据分析、KS学习。在包括 AOS/JOE/Biometrika 在内的国际一流统计期刊上发表论文30余篇。主持国家自然科学基金重点项目子课题、面上项目、青年项目各1项。参与国家重点研发计划重点专项课题2项和上海市重点项目1项。曾获上海市自然科学二等奖。全国工业统计学教学研究会常务理事、数字经济与区块链技术分会副理事长，SCI期刊 Journal of Nonparametric Statistics 副主编。出版统计科普小说《统计王国奇遇记》。