arXiv Machine Learning

Nonparametric Contextual Pricing and Inventory Learning under Censored Demand

arXiv Statistics ML
2d ago

Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach

The paper presents a data‑driven framework for multi‑period lost‑sales inventory control when demand is censored, meaning stockouts only reveal that demand exceeded the stocking level. It introduces a new cost decomposition for base‑stock policies and a biased sample‑average approximation (SAA) method, leading to two algorithms: an upper‑biased SAA that achieves near‑optimal sample complexity under an offline coverage condition, and a lower‑biased SAA that actively generates coverage to achieve near‑optimal online regret. The biased SAA approach offers a general principle for applying pessimism and optimism in settings with censored feedback.

By Yuxuan Han, Xiaoyu Fan, Jiawei Zhang, Zhengyuan Zhou