# Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve

arXiv: https://arxiv.org/abs/1310.5665 · HTML: https://mlsanity.com/paper/1310.5665/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2013.

## Built on by (most cited first, 16 of 16)

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- [Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data](https://mlsanity.com/paper/2202.10944/) (2022)
- [Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization](https://mlsanity.com/paper/2510.19591/) (2025)
- [Support-aware offline policy selection for advertising marketplaces](https://mlsanity.com/paper/2605.21736/) (2026)
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## Abstract (arXiv)

Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.