时 间：2023年5月24日（周三）10：50-11：30
地 点: 理科大楼A1514室
题目：High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection
报告人：蒋斐宇 复旦大学副研究员
主持人：章迎莹 副教授
主 办：统计学院
摘 要：
We consider a high-dimensional dynamic pricing problem under non-stationarity, where a firm sells products to T sequentially arrived consumers that behave according to an unknown demand model that may change at unknown locations over time. The demand model is assumed to be a sparse high-dimensional generalized linear model(GLM), allowing for a feature vector that encodes products and consumer information. To achieve optimal revenue(i.e. least regret), the seller needs to learn and exploit the unknown GLM model while at the same time monitoring for potential change-points (CP). To tackle such a problem, we first design a novel penalized likelihood based online CP detection algorithm for high-dimensional GLM, which is the first in the CP literature that achieves optimal minimax localization error rate for high-dimensional GLM. A CP detection augmented dynamic pricing policy named CPDP is further proposed, which achieves a regret of order O(slog(Td)sqrt{MT}), where s is the sparsity level and M is the number of CP. Somewhat surprisingly, this regret order can be independent of the magnitude of the change size. A matching lower bound is further provided to show the optimality of CPDP (up to logarithmic factors). In particular, the optimality w.r.t. the number of CP M is the first in the dynamic pricing literature, and is achieved via a novel accelerated exploration mechanism. Extensive simulation experiments and a real data application on online lending illustrate the efficiency and practical value of the proposed policy.
报告人简介：
蒋斐宇是复旦大学管理学院统计与数据科学系青年副研究员，博士毕业于清华大学统计学研究中心。主要研究方向为非线性时间序列、变点分析、非参数和半参数计量等。研究成果发表于JRSSB，JOE，Sinica等期刊。