# Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing

arXiv: https://arxiv.org/abs/1801.10242 · HTML: https://mlsanity.com/paper/1801.10242/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 17 of 17)

- [Designing Decision Support Systems Using Counterfactual Prediction Sets](https://mlsanity.com/paper/2306.03928/) (2023)
- [Distribution-free Contextual Dynamic Pricing](https://mlsanity.com/paper/2109.07340/) (2021)
- [PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison](https://mlsanity.com/paper/2211.16110/) (2022)
- [Towards Human-AI Complementarity in Matching Tasks](https://mlsanity.com/paper/2508.13285/) (2025)
- [Linear Bandits with Memory: from Rotting to Rising](https://mlsanity.com/paper/2302.08345/) (2023)
- [Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards](https://mlsanity.com/paper/2304.13593/) (2023)
- [Stochastic Bandits for Egalitarian Assignment](https://mlsanity.com/paper/2410.05856/) (2024)
- [Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System](https://mlsanity.com/paper/2204.05782/) (2022)
- [Batched Stochastic Bandit for Nondegenerate Functions](https://mlsanity.com/paper/2405.05733/) (2024)
- [Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio](https://mlsanity.com/paper/2407.13687/) (2024)
- [Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality](https://mlsanity.com/paper/2510.12152/) (2025)
- [Empirical Bayesian Multi-Bandit Learning](https://mlsanity.com/paper/2510.26284/) (2025)
- [DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization](https://mlsanity.com/paper/2512.03607/) (2025)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem](https://mlsanity.com/paper/2302.02033/) (2023)
- [Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets](https://mlsanity.com/paper/2602.00188/) (2026)
- [Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions](https://mlsanity.com/paper/2608.26755/) (2026)

## Abstract (arXiv)

We consider dynamic pricing with many products under an evolving but low-dimensional demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimization with side information given by the observed demands. We design dynamic pricing algorithms whose revenue approaches that of the best fixed price vector in hindsight, at a rate that only depends on the intrinsic rank of the demand model and not the number of products. Our approach applies a bandit convex optimization algorithm in a projected low-dimensional space spanned by the latent product features, while simultaneously learning this span via online singular value decomposition of a carefully-crafted matrix containing the observed demands.