Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Vapnik-Chervonenkis dimension of neural networks
The handbook of brain theory and neural networks
Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Competitive generalized auctions
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Online learning in online auctions
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
Reducing truth-telling online mechanisms to online optimization
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Multi-unit auctions with budget-constrained bidders
Proceedings of the 6th ACM conference on Electronic commerce
Collusion-resistant mechanisms for single-parameter agents
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On profit-maximizing envy-free pricing
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Combination can be hard: approximability of the unique coverage problem
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Single-minded unlimited supply pricing on sparse instances
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Approximation algorithms and online mechanisms for item pricing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Approximating revenue-maximizing combinatorial auctions
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
How to sell a graph: guidelines for graph retailers
WG'06 Proceedings of the 32nd international conference on Graph-Theoretic Concepts in Computer Science
On the competitive ratio of the random sampling auction
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Near-optimal pricing in near-linear time
WADS'05 Proceedings of the 9th international conference on Algorithms and Data Structures
Pricing guidance in ad sale negotiations: the PrintAds example
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Incentive compatible regression learning
Journal of Computer and System Sciences
On the competitive ratio of online sampling auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Prior-free auctions with ordered bidders
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Mechanism Design via Consensus Estimates, Cross Checking, and Profit Extraction
ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
On the Competitive Ratio of Online Sampling Auctions
ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
Prior-free auctions for budgeted agents
Proceedings of the fourteenth ACM conference on Electronic commerce
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We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of revenue-maximizing pricing problems. Our reductions imply that for these problems, given an optimal (or @b-approximation) algorithm for an algorithmic pricing problem, we can convert it into a (1+@e)-approximation (or @b(1+@e)-approximation) for the incentive-compatible mechanism design problem, so long as the number of bidders is sufficiently large as a function of an appropriate measure of complexity of the class of allowable pricings. We apply these results to the problem of auctioning a digital good, to the attribute auction problem which includes a wide variety of discriminatory pricing problems, and to the problem of item-pricing in unlimited-supply combinatorial auctions. From a machine learning perspective, these settings present several challenges: in particular, the ''loss function'' is discontinuous, is asymmetric, and has a large range. We address these issues in part by introducing a new form of covering-number bound that is especially well-suited to these problems and may be of independent interest.