Learning to Price and Stock Under Contextual and Censored Demand

Abstract:To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market conditions where demands are influenced by various contextual factors, while simultaneously dealing with the difficulty of lost sales that obscure true demand information. However, existing approaches often fail to account for both contextual information and censored demand observations. We address this gap by presenting a framework where we model demand as a linear combination of basis functions with unknown coefficients, allowing for adaptive pricing and inventory decisions that respond to changing contexts. We propose an efficient algorithm to achieve regret bound $\mathcal{O}(K\sqrt{T}\log T)$ under concave revenue conditions and $\mathcal{O}(K^{2/3}T^{2/3}(\log T)^{1/2})$ for the general case, with matching lower bounds confirming optimality. Extensive numerical experiments across diverse scenarios demonstrate our algorithm's effectiveness.

Machine Learning (cs.LG)

arXiv:2609.06083 [cs.LG]

(or arXiv:2609.06083v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.06083 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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