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Published Articles >> Table of Contents >> Abstract
46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05)
pp. 605-614
Mechanism Design via Machine Learning
Maria-Florina Balcan, Carnegie Mellon University
Avrim Blum, Carnegie Mellon University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SFCS.2005.50
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| Abstract |
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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 wide variety of revenue-maximizing pricing problems. Our reductions imply that for these problems, given an optimal (or \beta-approximation) algorithm for the standard algorithmic problem, we can convert it into a (1+ \in)-approximation (or \beta(1+ \in)-approximation) for the incentive-compatiblemechanism design problem, so long as the number of bidders is sufficiently large as a function of an appropriate measure of complexity of the comparison class of solutions. We apply these results to the problem of auctioning a digital good, the attribute auction problem, and to the problem of itempricing in unlimited-supply combinatorial auctions. From a learning perspective, these settings present several challenges: in particular, the loss function is discontinuous and asymmetric, and the range of bidders’ valuations may be large.
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 Additional Information
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Citation:
Maria-Florina Balcan, Avrim Blum,
"Mechanism Design via Machine Learning,"
focs,
pp. 605-614,
46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05),
2005
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