# Learning to Price Homogeneous Data

arXiv: https://arxiv.org/abs/2407.05484 · HTML: https://mlsanity.com/paper/2407.05484/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Combinatorial Multi-Armed Bandit with General Reward Functions](https://mlsanity.com/paper/1610.06603/) (2016)
- [Dynamic Pricing in High-dimensions](https://mlsanity.com/paper/1609.07574/) (2016)
- [Learning Equilibria in Matching Markets from Bandit Feedback](https://mlsanity.com/paper/2108.08843/) (2021)
- [Oracle-Efficient Online Learning and Auction Design](https://mlsanity.com/paper/1611.01688/) (2016)
- [Logarithmic Regret in Feature-based Dynamic Pricing](https://mlsanity.com/paper/2102.10221/) (2021)
- [Perishability of Data: Dynamic Pricing under Varying-Coefficient Models](https://mlsanity.com/paper/1701.03537/) (2017)
- [Towards Data Auctions with Externalities](https://mlsanity.com/paper/2003.08345/) (2020)
- [New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs](https://mlsanity.com/paper/2302.11700/) (2023)
- [The Complexity of Optimal Multidimensional Pricing](https://mlsanity.com/paper/1311.2138/) (2013)
- [Pricing Ordered Items](https://mlsanity.com/paper/2106.04704/) (2021)
- [Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty](https://mlsanity.com/paper/2302.09700/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals](https://mlsanity.com/paper/2505.23124/) (2025)
- [NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty](https://mlsanity.com/paper/2605.01745/) (2026)

## Abstract (arXiv)

We study a data pricing problem, where a seller has access to N homogeneous data points (e.g. drawn i.i.d. from some distribution). There are m types of buyers in the market, where buyers of the same type i have the same valuation curve vi:[N]rightarrow [0,1], where vi(n) is the value for having n data points. A priori, the seller is unaware of the distribution of buyers, but can repeat the market for T rounds so as to learn the revenue-optimal pricing curve p:[N] rightarrow [0, 1]. To solve this online learning problem, we first develop novel discretization schemes to approximate any pricing curve. When compared to prior work, the size of our discretization schemes scales gracefully with the approximation parameter, which translates to better regret in online learning. Under assumptions like smoothness and diminishing returns which are satisfied by data, the discretization size can be reduced further. We then turn to the online learning problem, both in the stochastic and adversarial settings. On each round, the seller chooses an anonymous pricing curve pt. A new buyer appears and may choose to purchase some amount of data. She then reveals her type only if she makes a purchase. Our online algorithms build on classical algorithms such as UCB and FTPL, but require novel ideas to account for the asymmetric nature of this feedback and to deal with the vastness of the space of pricing curves. Using the improved discretization schemes previously developed, we are able to achieve tildeO(m√T) regret in the stochastic setting and tildeO(m3/2√T) regret in the adversarial setting.