SPEAKER：Dr. BIAN, Zeyu (Florida State University)

TIME/DATE：2025.9.19   10:00

CLASSROOM：A502

ABSTRACT

This work 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.

GUEST BIO

Zeyu Bian is an Assistant Professor in the Department of Statistics at Florida State University. He received his Ph.D. in Biostatistics from McGill University in 2022. His research focuses on reinforcement learning and causal inference, with applications in dynamic and personalized pricing.