Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
An approximate truthful mechanism for combinatorial auctions with single parameter agents
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Collusion-resistant mechanisms for single-parameter agents
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Optimal mechanism design and money burning
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Reducing mechanism design to algorithm design via machine learning
Journal of Computer and System Sciences
On random sampling auctions for digital goods
Proceedings of the 10th ACM conference on Electronic commerce
Revenue maximization with a single sample
Proceedings of the 11th ACM conference on Electronic commerce
Proceedings of the 12th ACM conference on Electronic commerce
Prior-free auctions for budgeted agents
Proceedings of the fourteenth ACM conference on Electronic commerce
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There is only one technique for prior-free optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the consensus and profit extraction techniques from Goldberg and Hartline [2003] to structurally rich environments that include, for example, single-minded combinatorial auctions.