时 间：2025年9月17日（周三）14：00 – 15：00
地 点：中北理科大楼A1514室
报告人：卞泽宇 佛罗里达州立大学 助理教授
主持人：章迎莹 华东师范大学 副教授
摘 要：
This paper studies offline dynamic pricing without data coverage assumption, thereby allowing for any price including the optimal one not being observed in the offline data. Previous approaches that rely on the various coverage assumptions such as that the optimal prices are observable, would lead to suboptimal decisions and consequently, reduced profits. We address this challenge by framing the problem to a partial identification framework. Specifically, we establish a partial identification bound for the demand parameter whose associated price is unobserved by leveraging the inherent monotonicity property in the pricing problem. We further incorporate pessimistic and opportunistic strategies within the proposed partial identification framework to derive the estimated policy. Theoretically, we establish rate-optimal finite-sample regret guarantees for both strategies. Empirically, we demonstrate the superior performance of the newly proposed methods via a synthetic environment. This research provides practitioners with valuable insights into offline pricing strategies in the challenging no-coverage setting, ultimately fostering sustainable growth and profitability of the company.
报告人简介：
卞泽宇，麦吉尔大学生物统计学博士，现任美国佛罗里达州立大学统计系助理教授。研究方向包括强化学习、因果推断、政策学习、动态治疗方案和动态定价。相关研究成果已发表在 Journal of the American Statistical Association、Biometrics、Journal of the Royal Statistical Society: Series C、Biostatistics 等统计学期刊。