时间：2019年11月12日(周二)13:30
地点：一教--314
报告人：谈飞 博士
专家介绍：
谈飞博士，于2019年5月获得美国新泽西理工计算机博士学位，博士论文获得计算机系Joseph Leung奖。现于美国Verizon Media集团下的雅虎研究院(纽约曼哈顿)任职研究科学家，主要研究兴趣是数据挖掘，机器学习应用和自然语言处理。他在相关的主流会议和期刊上发表十余篇学术论文，包括IEEE TNNLS (影响因子11.68), IEEE ICDM, SIAM SDM, IJCAI, Data Mining and Knowledge Discovery, Physical Review E, Europhysics Letters等。他也是一项在申请的美国专利共同持有人。他曾在Adobe(硅谷圣何塞总部)和雅虎研究院实习工作多次以及作为研究助理访问香港理工大学。
报告内容
Machine learning has being harnessed to refine big data and render it value like never before. In this talk, we will explain three data monetization cases through machine learning. Specifically, in online lending, how to represent two competing risks, charge-off and prepayment, in funded loans is a fundamental problem behind ROI maximization. We develop a hierarchical grading framework to integrate them both qualitatively and quantitatively. In addition, in digital marketing, we propose to treat content understanding as to elucidate their causal implications in driving user responses. A flexible and adaptive doubly robust estimator is introduced to identify the causality between related features and user responses from observational data. In online communities, abusive language has profound impacts on their integrity. We explore two byte-level quantization schemes for character representation. The primitive representation empowers models to capture signals underlying multi-byte characters of online posts elegantly.
