  In mechanism design, it is challenging to design the optimal auction with
correlated values in general settings. Although value distribution can be
further exploited to improve revenue, the complex correlation structure makes
it hard to acquire in practice. Data-driven auction mechanisms, powered by
machine learning, enable to design auctions directly from historical auction
data, without relying on specific value distributions. In this work, we design
a learning-based auction, which can encode the correlation of values into the
rank score of each bidder, and further adjust the ranking rule to approach the
optimal revenue. We strictly guarantee the property of strategy-proofness by
encoding game theoretical conditions into the neural network structure.
Furthermore, all operations in the designed auctions are differentiable to
enable an end-to-end training paradigm. Experimental results demonstrate that
the proposed auction mechanism can represent almost any strategy-proof auction
mechanism, and outperforms the auction mechanisms wildly used in the correlated
value settings.
