讲座信息

讲座信息

您当前的位置: 讲座信息
20191016 赵彦云、吕晓玲、尹建鑫、李扬:学术交流支持计划返校交流
时间:2019-10-13

报告时间:2019 / 10 / 16(周三) 9:00 - 10:30

报告地点:明德主楼 1016 会议室

报告人:赵彦云、吕晓玲、尹建鑫、李扬


报告简介:

报告题目:2019第62届ISI国际统计大会参感

报告摘要:第62届ISI国际统计大会于2019年8月18-23日在吉隆坡举行,来自114个国家2200多人参加,315场学术会议。将对大会特邀报告及整体情况,以及关心的互联网统计对政府统计改革与发展影响做一些介绍,也将阐述个人的研究观点。

报告人:赵彦云


Title: Data-driven Stochastic Optimization Models

Abstract: In this talk, I first introduce two optimization models. The first one is vehicle routing problem with pickup and delivery (VRPPD). In most existing settings, all of the demands being strictly satisfied can lead to longer routes and add operational costs. In this paper, we propose a new model with unserved demands, a relaxation formulation of demands satisfying constraints. We design a distributed ant colony optimization (ACO) based algorithm with some specific modifications to increase its efficiency for the proposed model. The second one is a rental network design model. We propose a Benders decomposition-based algorithm to solve this problem. Then I extend these two models to the framework of data-driven stochastic optimization problems. Finally, two possible applications of such models on nanofibers and greenhouse environmental control are discussed.

报告人:吕晓玲


Title: The VC Bound for FILTER Model
Abstract: In this talk, we will first introduce the FusIon penalized Logistic ThrEshold Regression(FILTER)  model with its origin, application scenario, as well as some theoretical guarantees on estimation and prediction. Particularly, we give a VC bound for the FILTER model, which will lead to a lower bound on the excess risk for the classification model.

报告人:尹建鑫


Title: Integrative Interaction Analysis using Threshold Gradient Directed Regularization
Abstract: For many complex business and industry problems, high-dimensional data collection and modeling have been conducted. It has been shown that interactions may have important implications beyond the main effects. The number of unknown parameters in an interaction analysis can be larger or much larger than the sample size. As such, results generated from analyzing a single data set are often unsatisfactory. Integrative analysis, which jointly analyzes the raw data from multiple independent studies, has been conducted in a series of recent studies and shown to outperform single–data set analysis, meta-analysis, and other multi–data set analyses. In this study, our goal is to conduct integrative analysis in interaction analysis. For regularized estimation and selection of important interactions (and main effects), we apply a threshold gradient directed regularization approach. Advancing from the existing studies, the threshold gradient directed regularization approach is modified to respect the “main effects, interactions” hierarchy. The proposed approach has an intuitive formulation and is computationally simple and broadly applicable. Simulations and the analyses of financial early warning system data and news-APP recommendation behavior data demonstrate its satisfactory practical performance.

报告人:李扬