  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.
