The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization
Author(s): Gonczarowski, Yannai A; Weinberg, S Matthew
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Gonczarowski, Yannai A | - |
dc.contributor.author | Weinberg, S Matthew | - |
dc.date.accessioned | 2021-10-08T19:48:08Z | - |
dc.date.available | 2021-10-08T19:48:08Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.citation | Gonczarowski, Yannai A., and S. Matthew Weinberg. "The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization." In Annual Symposium on Foundations of Computer Science (FOCS) (2018): pp. 416-426. doi:10.1109/FOCS.2018.00047 | en_US |
dc.identifier.issn | 1523-8288 | - |
dc.identifier.uri | https://arxiv.org/pdf/1808.02458v1.pdf | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr14p0p | - |
dc.description.abstract | We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multi-dimensional settings. Specifically, we study the standard model of n additive bidders whose values for m heterogeneous items are drawn independently. For any such instance and any ε>0, we show that it is possible to learn an ε-Bayesian Incentive Compatible auction whose expected revenue is within ε of the optimal ε-BIC auction from only polynomially many samples. Our approach is based on ideas that hold quite generally, and completely sidestep the difficulty of characterizing optimal (or near-optimal) auctions for these settings. Therefore, our results easily extend to general multi-dimensional settings, including valuations that aren't necessarily even subadditive, and arbitrary allocation constraints. For the cases of a single bidder and many goods, or a single parameter (good) and many bidders, our analysis yields exact incentive compatibility (and for the latter also computational efficiency). Although the single-parameter case is already well-understood, our corollary for this case extends slightly the state-of-the-art. | en_US |
dc.format.extent | 416 - 426 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | Annual Symposium on Foundations of Computer Science | en_US |
dc.rights | Author's manuscript | en_US |
dc.title | The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization | en_US |
dc.type | Conference Article | en_US |
dc.identifier.doi | 10.1109/FOCS.2018.00047 | - |
dc.identifier.eissn | 2575-8454 | - |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding | en_US |
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SampleComplexityDimensionalRevenueMax.pdf | 573.62 kB | Adobe PDF | View/Download |
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