时 间：2023年12月7日 9:00-10:00
地 点：普陀校区理科大楼A1514
报告人：骆钇澐 上海财经大学助理教授
主持人：石芸华东师范大学副教授
摘 要：
Dynamic pricing is a fast-moving research area in machine learning and operations management. A lot of work has been done for this problem with known noise. In this paper, we consider a contextual dynamic pricing problem under a linear customer valuation model with an unknown market noise distribution F. This problem is very challenging due to the difficulty in balancing three tangled tasks of
revenue-maximization, estimating the linear valuation parameter \theta_{0}, and learning the nonparametric F. To address this issue, we develop a novel Explore-then-UCB (ExUCB) strategy that includes an exploration for \theta_{0}-learning and a followed UCB procedure of joint revenue-maximization and F-learning. Under Lipschitz and 2nd-order smoothness assumptions on F, ExUCB is the first approach to achieve the O(T^{2/3}) regret rate. Under the Lipschitz assumption only, ExUCB matches the best existing regret of ~O(T3=4) and is computationally more efficient. Furthermore, for regret lower bounds under the nonparametric F, not much work has been done beyond only assuming Lipschitz. To fill this gap, we provide the first \Omega(T^{3/5}) lower bound under Lipschitz and 2nd-order smoothness assumptions. This is a joint work with Will Wei Sun, Purdue University and Yufeng Liu, University of North Carolina at Chapel Hill.
报告人简介：
骆钇澐博士现任教于上海财经大学统计与管理学院，主要从事在线学习，老虎机算法，动态定价，动态搭配等研究。其主要学术成果发表在 Mathematics of Operations Research, NeurIPS, Canadian Journal of Statistics等国际著名学术期刊和会议上。骆博士曾先后就读于北京大学和北卡罗来纳大学教堂山分校。