Efficient empirical revenue maximization in single-parameter auction environments

DOI10.1145/3055399.3055427zbMath1370.68252arXiv1610.09976OpenAlexW2544285741MaRDI QIDQ4978029

Yannai A. Gonczarowski, Noam Nisan

Publication date: 17 August 2017

Published in: Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.09976

zbMATH Keywords

PAC learningapproximate revenue maximization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Auctions, bargaining, bidding and selling, and other market models (91B26)

Related Items (4)

Bayesian auctions with efficient queries ⋮ Improved two sample revenue guarantees via mixed-integer linear programming ⋮ Unnamed Item ⋮ Learning in Repeated Auctions

This page was built for publication: Efficient empirical revenue maximization in single-parameter auction environments