# Contextual Dynamic Pricing with Heterogeneous Buyers

arXiv: https://arxiv.org/abs/2512.09513 · HTML: https://mlsanity.com/paper/2512.09513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [The Statistical Complexity of Interactive Decision Making](https://mlsanity.com/paper/2112.13487/) (2021)
- [Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective](https://mlsanity.com/paper/2010.03104/) (2020)
- [Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning](https://mlsanity.com/paper/2110.00871/) (2021)
- [Dynamic Pricing in High-dimensions](https://mlsanity.com/paper/1609.07574/) (2016)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting](https://mlsanity.com/paper/1902.01520/) (2019)
- [Multidimensional Binary Search for Contextual Decision-Making](https://mlsanity.com/paper/1611.00829/) (2016)
- [Contextual Search via Intrinsic Volumes](https://mlsanity.com/paper/1804.03195/) (2018)
- [Dynamic Pricing with Finitely Many Unknown Valuations](https://mlsanity.com/paper/1807.03288/) (2018)
- [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)
- [Perishability of Data: Dynamic Pricing under Varying-Coefficient Models](https://mlsanity.com/paper/1701.03537/) (2017)
- [Pricing Query Complexity of Revenue Maximization](https://mlsanity.com/paper/2111.03158/) (2021)
- [Contextual Search in the Presence of Adversarial Corruptions](https://mlsanity.com/paper/2002.11650/) (2020)
- [Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond](https://mlsanity.com/paper/2503.01701/) (2025)
- [Corruption-Robust Lipschitz Contextual Search](https://mlsanity.com/paper/2307.13903/) (2023)

## Abstract (arXiv)

We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over T rounds) that depend on the observable d-dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support size Kstar. We develop a contextual pricing algorithm based on optimistic posterior sampling with regret widetildeO(Kstar√dT), which we prove to be tight in d and T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on Kstar.