Making the Most of Your Samples

@article{Huang2015MakingTM,
  title={Making the Most of Your Samples},
  author={Zhiyi Huang and Y. Mansour and T. Roughgarden},
  journal={Proceedings of the Sixteenth ACM Conference on Economics and Computation},
  year={2015}
}
We study the problem of setting a price for a potential buyer with a valuation drawn from an unknown distribution D. The seller has "data" about D in the form of m ≥ 1 i.i.d. samples, and the algorithmic challenge is to use these samples to obtain expected revenue as close as possible to what could be achieved with advance knowledge of D. Our first set of results quantifies the number of samples m that are necessary and sufficient to obtain a (1-ε)-approximation. For example, for an unknown… Expand
The sample complexity of revenue maximization
Sample complexity of single-parameter revenue maximization
Prior-Independent Optimal Auctions
Learning Multi-Item Auctions with (or without) Samples
  • Yang Cai, C. Daskalakis
  • Mathematics, Computer Science
  • 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)
  • 2017
Bayesian Auctions with Efficient Queries
Prior-Independent Optimal Auctions
Robust Revenue Maximization Under Minimal Statistical Information
G T ] 6 A ug 2 02 0 Bayesian Auctions with Efficient Queries ∗
...
1
2
3
4
5
...

References

SHOWING 1-2 OF 2 REFERENCES
Auctions versus Negotiations
A theory of the learnable
  • L. Valiant
  • Mathematics, Computer Science
  • STOC '84
  • 1984