18 citations · 55 across the 7 of their papers we have counts for
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cs.GT2020
iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding
Benjamin Lubin, Manuel Beyeler, Gianluca Brero +1
Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learni…
cs.GT2020★ 10 cited
Fourier Analysis-based Iterative Combinatorial Auctions
Jakob Weissteiner, Chris Wendler, Sven Seuken +2
Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of…