# Learning Reserve Prices in Second-Price Auctions

arXiv: https://arxiv.org/abs/1912.10069 · HTML: https://mlsanity.com/paper/1912.10069/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Are Bounded Contracts Learnable and Approximately Optimal?](https://mlsanity.com/paper/2402.14486/) (2024)
- [Bandit Sequential Posted Pricing via Half-Concavity](https://mlsanity.com/paper/2312.12794/) (2023)
- [Learning Optimal Posted Prices for a Unit-Demand Buyer](https://mlsanity.com/paper/2506.02284/) (2025)
- [Pricing Query Complexity of Multiplicative Revenue Approximation](https://mlsanity.com/paper/2602.10483/) (2026)

## Abstract (arXiv)

This paper proves the tight sample complexity of sf Second-Price Auction with Anonymous Reserve, up to a logarithmic factor, for each of all the value distribution families studied in the literature: [0,, 1]-bounded, [1,, H]-bounded, regular, and monotone hazard rate (MHR). Remarkably, the setting-specific tight sample complexity mathsfpoly(varepsilon-1) depends on the precision varepsilon ∈ (0, 1), but not on the number of bidders n ≥ 1. Further, in the two bounded-support settings, our learning algorithm allows em correlated value distributions. In contrast, the tight sample complexity tildeTheta(n) · mathsfpoly(varepsilon-1) of sf Myerson Auction proved by Guo, Huang and Zhang (STOC~2019) has a nearly-linear dependence on n ≥ 1, and holds only for em independent value distributions in every setting. We follow a similar framework as the Guo-Huang-Zhang work, but replace their information theoretical arguments with a direct proof.