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Pittsburgh, Pennsylvania, USA
Oct. 23, 2005 to Oct. 25, 2005
ISBN: 0-7695-2468-0
pp: 605-614
Maria-Florina Balcan , Carnegie Mellon University
Avrim Blum , Carnegie Mellon University
<p>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. </p>
Maria-Florina Balcan, Avrim Blum, "Mechanism Design via Machine Learning", FOCS, 2005, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science 2005, pp. 605-614, doi:10.1109/SFCS.2005.50
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