【专家简介】：Yining Wang，美国德克萨斯大学达拉斯分校纳维恩金达尔管理学院运营管理副教授，毕业于卡内基梅隆大学，获得机器学习博士学位。研究方向主要集中在机器学习和在线学习方法论，并在运营和收入管理中应用。也对在个性化收入管理系统中使用机器学习和人工智能所产生的伦理问题感兴趣，例如数据隐私保护和决策公平问题。
【报告摘要】：The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. We also extend the framework to a more stringent privacy protection notion (the local privacy) and nonparametric modeling of demand rates in a follow-up paper.
时 间：2023年6月7日 13:30
地 点：位育楼417
