arXiv Machine Learning By Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi

Adaptive Inference for Resource-Constrained Dynamic Pricing

Read the original on arXiv Machine Learning →

arXiv:2606. 03736v2 Announce Type: replace-cross Abstract: We study resource-constrained dynamic pricing when the seller seeks revenue and valid inference about demand at a price fixed before the selling season.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 3

Resource-Constrained Adaptive Inference for Sequential Pricing

arXiv:2606. 03736v1 Announce Type: cross Abstract: Resource-constrained pricing controllers can make fixed-price inference impossible: the controller's resource state may remove the target price neighborhood from the feasible set, even when every realized action has a known positive density.

By Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
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