  In this paper, we study the design of deep learning-powered iterative
combinatorial auctions (ICAs). We build on prior work where preference
elicitation was done via kernelized support vector regressions (SVRs). However,
the SVR-based approach has limitations because it requires solving a machine
learning (ML)-based winner determination problem (WDP). With expressive kernels
(like gaussians), the ML-based WDP cannot be solved for large domains. While
linear or quadratic kernels have better computational scalability, these
kernels have limited expressiveness. In this work, we address these
shortcomings by using deep neural networks (DNNs) instead of SVRs. We first
show how the DNN-based WDP can be reformulated into a mixed integer program
(MIP). Second, we experimentally compare the prediction performance of DNNs
against SVRs. Third, we present experimental evaluations in two medium-sized
domains which show that even ICAs based on relatively small-sized DNNs lead to
higher economic efficiency than ICAs based on kernelized SVRs. Finally, we show
that our DNN-powered ICA also scales well to very large CA domains.
