# Semi-parametric dynamic contextual pricing

arXiv: https://arxiv.org/abs/1901.02045 · HTML: https://mlsanity.com/paper/1901.02045/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

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

- [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)
- [Improved Algorithms for Contextual Dynamic Pricing](https://mlsanity.com/paper/2406.11316/) (2024)
- [Stateful Offline Contextual Policy Evaluation and Learning](https://mlsanity.com/paper/2110.10081/) (2021)
- [Learning to Price Against a Moving Target](https://mlsanity.com/paper/2106.04689/) (2021)
- [Pricing with Contextual Elasticity and Heteroscedastic Valuation](https://mlsanity.com/paper/2312.15999/) (2023)
- [Contractual Reinforcement Learning: Pulling Arms with Invisible Hands](https://mlsanity.com/paper/2407.01458/) (2024)
- [Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization](https://mlsanity.com/paper/2206.07528/) (2022)
- [A Parametric Contextual Online Learning Theory of Brokerage](https://mlsanity.com/paper/2407.01566/) (2024)
- [Online Pricing and Allocation with Demand Learning and Fulfillment Cost](https://mlsanity.com/paper/2501.18049/) (2025)
- [Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices](https://mlsanity.com/paper/2501.14155/) (2025)
- [Contextual Online Bilateral Trade](https://mlsanity.com/paper/2602.12903/) (2026)
- [Dynamic Assortment Selection and Pricing with Censored Preference Feedback](https://mlsanity.com/paper/2504.02324/) (2025)
- [Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand](https://mlsanity.com/paper/2605.05609/) (2026)
- [A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design](https://mlsanity.com/paper/2210.10278/) (2022)
- [Dynamic pricing with Bayesian updates from online reviews](https://mlsanity.com/paper/2404.14953/) (2024)
- [Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio](https://mlsanity.com/paper/2407.13687/) (2024)

## Abstract (arXiv)

Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with tilde O(√n) regret. We empirically test a scalable implementation of our algorithm and observe good performance.