# Improved Algorithms for Contextual Dynamic Pricing

arXiv: https://arxiv.org/abs/2406.11316 · HTML: https://mlsanity.com/paper/2406.11316/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Online Learning with Feedback Graphs: Beyond Bandits](https://mlsanity.com/paper/1502.07617/) (2015)
- [Dynamic Pricing in High-dimensions](https://mlsanity.com/paper/1609.07574/) (2016)
- [Contextual Bandits with Cross-learning](https://mlsanity.com/paper/1809.09582/) (2018)
- [Dynamic Pricing with Finitely Many Unknown Valuations](https://mlsanity.com/paper/1807.03288/) (2018)
- [The Elliptical Potential Lemma Revisited](https://mlsanity.com/paper/2010.10182/) (2020)
- [Policy Optimization Using Semi-parametric Models for Dynamic Pricing](https://mlsanity.com/paper/2109.06368/) (2021)
- [Distribution-free Contextual Dynamic Pricing](https://mlsanity.com/paper/2109.07340/) (2021)
- [Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise](https://mlsanity.com/paper/2201.11341/) (2022)
- [Semi-parametric dynamic contextual pricing](https://mlsanity.com/paper/1901.02045/) (2019)
- [Optimal cross-learning for contextual bandits with unknown context distributions](https://mlsanity.com/paper/2401.01857/) (2024)
- [Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/2007.02470/) (2020)

## Built on by (most cited first, 11 of 11)

- [Dynamic Pricing in the Linear Valuation Model using Shape Constraints](https://mlsanity.com/paper/2502.05776/) (2025)
- [Dynamic Pricing with Adversarially-Censored Demands](https://mlsanity.com/paper/2502.06168/) (2025)
- [Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift](https://mlsanity.com/paper/2505.17203/) (2025)
- [Efficient Adversarial Attacks on High-dimensional Offline Bandits](https://mlsanity.com/paper/2602.01658/) (2026)
- [Nonparametric Contextual Online Bilateral Trade](https://mlsanity.com/paper/2602.12904/) (2026)
- [Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand](https://mlsanity.com/paper/2605.05609/) (2026)
- [Online Linear Regression with Paid Stochastic Features](https://mlsanity.com/paper/2511.08073/) (2025)
- [Adaptive Bandit Algorithms for Contextual Matching Markets](https://mlsanity.com/paper/2605.28290/) (2026)
- [Contextual Learning for Stochastic Optimization](https://mlsanity.com/paper/2505.16829/) (2025)
- [Learning to Attack: A Bandit Approach to Adversarial Context Poisoning](https://mlsanity.com/paper/2603.00567/) (2026)
- [NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty](https://mlsanity.com/paper/2605.01745/) (2026)

## Abstract (arXiv)

In contextual dynamic pricing, a seller sequentially prices goods based on contextual information. Buyers will purchase products only if the prices are below their valuations. The goal of the seller is to design a pricing strategy that collects as much revenue as possible. We focus on two different valuation models. The first assumes that valuations linearly depend on the context and are further distorted by noise. Under minor regularity assumptions, our algorithm achieves an optimal regret bound of tildeO(T2/3), improving the existing results. The second model removes the linearity assumption, requiring only that the expected buyer valuation is β-H"older in the context. For this model, our algorithm obtains a regret tildeO(Td+2β/d+3β), where d is the dimension of the context space.