Speaker: Yongyi Guo, Harvard University

Time: 9:00-10:00 am, December 1, 2022, GMT+8

Venue: Tecent Meeting ID: 402-713-114 (https://meeting.tencent.com/dm/WWImNdIoSNYM)

Abstract:

Dynamic pricing is one of the most common examples of online decision problems with continuous action space. With the development of e-commerce and the massive real-time data in online platforms today, feature-based (or contextual) pricing models have become increasingly important. In this work, we study the feature-based dynamic pricing problem where the market value of a product is linear in its observed features plus unobservable market noise whose distribution is unknown. To reduce modeling bias, we assume that the market noise density falls into a non-parametric class. We propose a dynamic statistical learning and decision making policy that minimizes regret by combining online decision making and semi-parametric statistical estimation from a generalized linear model with an unknown link. Specifically, we provide non-asymptotic uniform error bounds for kernel type regression estimators, which enable us to control the regret while learning the model efficiently. Under mild conditions, our proposed algorithm achieves near optimal regret at the same order as the lower bound when the market noise distribution is parametric (\Omega(\sqrt{T})). The performance of the algorithm is also demonstrated through intensive simulations and real data experiments.

Biography:

Yongyi Guo is a postdoctoral research fellow in the Department of Statistics, Harvard University, hosted by Professor Susan A. Murphy. In the fall of 2023, she will start as an assistant professor at the Department of Statistics, University of Wisconsin-Madison. Yongyi obtained her Ph.D. degree from the Department of Operations Research and Financial Engineering at Princeton University, advised by Professor Jianqing Fan. Before that, she obtained her Bachelor’s degree from the School of Mathematical Sciences, Peking University. Her research interests lie in statistics, machine learning and data-driven decision-making.

Source: School of Mathematical Sciences