# Learning Optimal Posted Prices for a Unit-Demand Buyer

arXiv: https://arxiv.org/abs/2506.02284 · HTML: https://mlsanity.com/paper/2506.02284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 30 of 31)

- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Sample Complexity of Automated Mechanism Design](https://mlsanity.com/paper/1606.04145/) (2016)
- [Settling the Sample Complexity of Single-parameter Revenue Maximization](https://mlsanity.com/paper/1904.04962/) (2019)
- [Multi-Item Mechanisms without Item-Independence: Learnability via Robustness](https://mlsanity.com/paper/1911.02146/) (2019)
- [Generalizing Complex Hypotheses on Product Distributions: Auctions, Prophet Inequalities, and Pandora's Problem](https://mlsanity.com/paper/1911.11936/) (2019)
- [The Sample Complexity of Auctions with Side Information](https://mlsanity.com/paper/1511.02296/) (2015)
- [Learning Multi-item Auctions with (or without) Samples](https://mlsanity.com/paper/1709.00228/) (2017)
- [The Sample Complexity of Up-to-varepsilon Multi-Dimensional Revenue Maximization](https://mlsanity.com/paper/1808.02458/) (2018)
- [Making the Most of Your Samples](https://mlsanity.com/paper/1407.2479/) (2014)
- [Pricing Query Complexity of Revenue Maximization](https://mlsanity.com/paper/2111.03158/) (2021)
- [Efficient Empirical Revenue Maximization in Single-Parameter Auction Environments](https://mlsanity.com/paper/1610.09976/) (2016)
- [A Sample Complexity Measure with Applications to Learning Optimal Auctions](https://mlsanity.com/paper/1704.02598/) (2017)
- [Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms](https://mlsanity.com/paper/1705.00243/) (2017)
- [Sample Complexity of Posted Pricing for a Single Item](https://mlsanity.com/paper/2406.00819/) (2024)
- [A Simple and Approximately Optimal Mechanism for an Additive Buyer](https://mlsanity.com/paper/1405.6146/) (2014)
- [A Duality-Based Unified Approach to Bayesian Mechanism Design](https://mlsanity.com/paper/1812.01577/) (2018)
- [Sampling and Representation Complexity of Revenue Maximization](https://mlsanity.com/paper/1402.4535/) (2014)
- [An n-to-1 Bidder Reduction for Multi-item Auctions and its Applications](https://mlsanity.com/paper/1406.3278/) (2014)
- [Mechanism Design via Optimal Transport](https://mlsanity.com/paper/1503.01958/) (2015)
- [Bandit Sequential Posted Pricing via Half-Concavity](https://mlsanity.com/paper/2312.12794/) (2023)
- [Selling Multiple Correlated Goods: Revenue Maximization and Menu-Size Complexity (old title: "The Menu-Size Complexity of Auctions")](https://mlsanity.com/paper/1304.6116/) (2013)
- [Learning Reserve Prices in Second-Price Auctions](https://mlsanity.com/paper/1912.10069/) (2019)
- [Learning with Comparison Feedback: Online Estimation of Sample Statistics](https://mlsanity.com/paper/2101.04176/) (2021)
- [Computing Simple Mechanisms: Lift-and-Round over Marginal Reduced Forms](https://mlsanity.com/paper/2111.03962/) (2021)
- [Strong Revenue (Non-)Monotonicity of Single-parameter Auctions](https://mlsanity.com/paper/2205.08062/) (2022)
- [Description Complexity of Regular Distributions](https://mlsanity.com/paper/2305.05590/) (2023)
- [Non-Stochastic CDF Estimation Using Threshold Queries](https://mlsanity.com/paper/2301.05682/) (2023)
- [Optimal Pricing is Hard](https://mlsanity.com/paper/1503.02516/) (2015)
- [Smoothed Analysis of Multi-Item Auctions with Correlated Values](https://mlsanity.com/paper/1811.12459/) (2018)
- [Menu-size Complexity and Revenue Continuity of Buy-many Mechanisms](https://mlsanity.com/paper/2003.10636/) (2020)

## Built on by (most cited first, 1 of 1)

- [Pricing Query Complexity of Multiplicative Revenue Approximation](https://mlsanity.com/paper/2602.10483/) (2026)

## Abstract (arXiv)

We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem.