arXiv Machine Learning

On Non-Stationary Dynamic Pricing: Adaptivity and Optimality

arXiv:2607. 24115v1 Announce Type: cross Abstract: We study the contextual dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model that can change over time.

arXiv Machine Learning
Sep 11

Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret without a Variance Bound

The paper studies contextual bilateral trade with full feedback, showing that action-independent observations eliminate the usual polynomial adaptation penalty seen in heavy-tailed bandits. It presents fully parameter-free algorithms that achieve oracle minimax regret rates without knowing the moment order or scale, and derives new regret bounds for both parametric and nonparametric settings. The key technical insight is a paired squared‑loss statistic whose noise cancels, enabling model selection and yielding regret rates that interpolate between classical nonparametric and linear extremes.

By Hangyi Zhao
arXiv Machine Learning
Aug 18

Dynamic Pricing and Advertising with Demand Learning

arXiv:2304. 14385v4 Announce Type: replace-cross Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product.

By Shipra Agrawal, Yiding Feng, Wei Tang
arXiv Machine Learning
Jul 7

Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions

arXiv:2607. 02891v1 Announce Type: new Abstract: Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve.

By Zihao Hu, Yuan Yao, Jiheng Zhang, Zhengyuan Zhou
arXiv Machine Learning
Jun 3

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).

By Oleg Miroshnichenko