Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Context-Integrated Transformer-Based Neural Network for Auction Design   One of the central problems in auction design is developing an
incentive-compatible mechanism that maximizes the auctioneer's expected
revenue. While theoretical approaches have encountered bottlenecks in
multi-item auctions, recently, there has been much progress on finding the
optimal mechanism through deep learning. However, these works either focus on a
fixed set of bidders and items, or restrict the auction to be symmetric. In
this work, we overcome such limitations by factoring \emph{public} contextual
information of bidders and items into the auction learning framework. We
propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural
network for optimal auction design, which maintains permutation-equivariance
over bids and contexts while being able to find asymmetric solutions. We show
by extensive experiments that $\mathtt{CITransNet}$ can recover the known
optimal solutions in single-item settings, outperform strong baselines in
multi-item auctions, and generalize well to cases other than those in training.

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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph