arXiv Machine Learning By Feiyu Jiang, Zifeng Zhao

On Non-Stationary Dynamic Pricing: Adaptivity and Optimality

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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.

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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