# On Non-Stationary Dynamic Pricing: Adaptivity and Optimality

arXiv: https://arxiv.org/abs/2607.24115 · HTML: https://mlsanity.com/paper/2607.24115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach](https://mlsanity.com/paper/2102.05406/) (2021)
- [Learning to Optimize under Non-Stationarity](https://mlsanity.com/paper/1810.03024/) (2018)
- [A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free](https://mlsanity.com/paper/1902.00980/) (2019)
- [A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem](https://mlsanity.com/paper/1711.03539/) (2017)
- [Hedging the Drift: Learning to Optimize under Non-Stationarity](https://mlsanity.com/paper/1903.01461/) (2019)
- [Tracking Most Significant Arm Switches in Bandits](https://mlsanity.com/paper/2112.13838/) (2021)
- [Multiscale Change-Point Inference](https://mlsanity.com/paper/1301.7212/) (2013)
- [Differential Privacy in Personalized Pricing with Nonparametric Demand Models](https://mlsanity.com/paper/2109.04615/) (2021)
- [On Adaptivity in Non-stationary Stochastic Optimization With Bandit Feedback](https://mlsanity.com/paper/2210.05584/) (2022)
- [On Dynamic Pricing with Covariates](https://mlsanity.com/paper/2112.13254/) (2021)
- [A review on minimax rates in change point detection and localisation](https://mlsanity.com/paper/2011.01857/) (2020)
- [High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection](https://mlsanity.com/paper/2303.07570/) (2023)
- [Pricing with Contextual Elasticity and Heteroscedastic Valuation](https://mlsanity.com/paper/2312.15999/) (2023)
- [Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints](https://mlsanity.com/paper/2406.02424/) (2024)
- [Localized exploration in contextual dynamic pricing achieves dimension-free regret](https://mlsanity.com/paper/2412.19252/) (2024)

## Abstract (arXiv)

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. The demand model is assumed to be a generalized linear model (GLM), allowing for a feature vector in ℝd that encodes products and consumer information. To achieve optimal revenue (i.e., least regret), the firm needs to learn and exploit the unknown GLMs while monitoring for potential changes. We propose a multiscale change-point detection based algorithm that achieves a regret of order widetildeO(√sTdTwedgeVT1/3d1/3T2/3+√dT), where sT is the number of piecewise stationary segments and VT is a newly defined notion of design-adjusted variation budget of model parameters. Our algorithm is adaptive and does not require knowing sT or VT. Moreover, to our knowledge, this is the first dynamic pricing algorithm that is adaptive to the nature of changes and achieves the best-of-both-worlds rate, thus closing a long-standing gap in the literature. We remark that, due to the varying contexts, existing works in the adaptive non-stationary bandit literature cannot be applied to achieve optimality for contextual dynamic pricing. The regret is further accompanied with a newly constructed minimax lower bound, confirming the optimality of our algorithm (up to logarithmic factors). Extensive numerical experiments are conducted to illustrate the efficiency and robustness of the proposed algorithm in non-stationary dynamic pricing.