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.

翻译：在机制设计中，设计具有相关价值的一般环境下的最优拍卖颇具挑战性。尽管可以进一步利用价值分布来提高收入，但复杂的相关结构在实践中难以获取。借助机器学习的数据驱动拍卖机制，能够直接从历史拍卖数据中设计拍卖，而无需依赖特定的价值分布。在本研究中，我们设计了一种基于学习的拍卖，能够将价值的相关性编码到每位竞拍者的排名分数中，并进一步调整排名规则以趋近最优收入。通过将博弈理论条件编码到神经网络结构中，我们严格保证了策略证明性。此外，所设计的拍卖中的所有操作都是可微的，从而实现了端到端的训练范式。实验结果表明，所提出的拍卖机制能够表示几乎任何策略证明性拍卖机制，并且在相关价值设置中优于广泛使用的拍卖机制。