# A Robust Multi-Item Auction Design with Statistical Learning

arXiv: https://arxiv.org/abs/2302.00941 · HTML: https://mlsanity.com/paper/2302.00941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Learning Simple Auctions](https://mlsanity.com/paper/1604.03171/) (2016)
- [Multi-Item Mechanisms without Item-Independence: Learnability via Robustness](https://mlsanity.com/paper/1911.02146/) (2019)
- [Learning Multi-item Auctions with (or without) Samples](https://mlsanity.com/paper/1709.00228/) (2017)
- [Understanding Incentives: Mechanism Design becomes Algorithm Design](https://mlsanity.com/paper/1305.4002/) (2013)
- [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)
- [The Menu-Size Complexity of Revenue Approximation](https://mlsanity.com/paper/1604.06580/) (2016)
- [Recommender Systems meet Mechanism Design](https://mlsanity.com/paper/2110.12558/) (2021)

## Abstract (arXiv)

We propose a novel statistical learning method for multi-item auctions that incorporates credible intervals. Our approach employs nonparametric density estimation to estimate credible intervals for bidder types based on historical data. We introduce two new strategies that leverage these credible intervals to reduce the time cost of implementing auctions. The first strategy screens potential winners' value regions within the credible intervals, while the second strategy simplifies the type distribution when the length of the interval is below a threshold value. These strategies are easy to implement and ensure fairness, dominant-strategy incentive compatibility, and dominant-strategy individual rationality with a high probability, while simultaneously reducing implementation costs. We demonstrate the effectiveness of our strategies using the Vickrey-Clarke-Groves mechanism and evaluate their performance through simulation experiments. Our results show that the proposed strategies consistently outperform alternative methods, achieving both revenue maximization and cost reduction objectives.