# Optimal Auctions for Correlated Buyers with Sampling

arXiv: https://arxiv.org/abs/1406.1571 · HTML: https://mlsanity.com/paper/1406.1571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2014.

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

- [Optimal Scoring Rule Design under Partial Knowledge](https://mlsanity.com/paper/2107.07420/) (2021)
- [Enhancing Affine Maximizer Auctions with Correlation-Aware Payment](https://mlsanity.com/paper/2602.09455/) (2026)

## Abstract (arXiv)

Cr'emer and McLean [1985] showed that, when buyers' valuations are drawn from a correlated distribution, an auction with full knowledge on the distribution can extract the full social surplus. We study whether this phenomenon persists when the auctioneer has only incomplete knowledge of the distribution, represented by a finite family of candidate distributions, and has sample access to the real distribution. We show that the naive approach which uses samples to distinguish candidate distributions may fail, whereas an extended version of the Cr'emer-McLean auction simultaneously extracts full social surplus under each candidate distribution. With an algebraic argument, we give a tight bound on the number of samples needed by this auction, which is the difference between the number of candidate distributions and the dimension of the linear space they span.